MODERN UNIVERSITY FOR TECHNOLOGY AND INFORMATION

B.Sc. (Hons) Computing with Pathways in: Computer Science Computer Information Systems

The Modules

Contents

Page 1

Section 1 Key personnel

Page 3

Section 2 Modules in the Basic Science Field

Page 62

Section 3 Modules in the Computer Science Field

Page 206

Section 4 Modules in the Information Systems Field

Section 1

Key personnel

1

Key Personnel: Faculty of Computer Science Prof. Dr. Nabil A. El-Deeb

Dean

Prof. Dr. Mohamed El Mayah

Vice Dean

Prof. Dr. Salah El Hawi Ali

Head of Computer Science Department

Prof. Dr. Hafez Salah Abd El-Wahab

Head of Information System Department

Prof. Dr. Eman Taha Abou El-Dahab

Head of Basic Science Department

2

Section 2 Modules in the Basic Science Field

3

Compulsory Modules in the Basic Sciences Field Module Code BS 111 BS 114 BS 141 BS 151 BS 152 BS 153 BS 154 BS 161 BS 251 BS 252 BS 253

Module Name English Language 1 Technical Report Writing Physics Linear Algebra I Linear Algebra II Mathematical Analysis I Mathematical Analysis II Applied Electronics Logics and Discrete Mathematics Numerical Analysis Probabilities and Statistics

Level 3 3 3 3 3 3 3 3 4 4 4

Credit Rating 10 10 10 10 10 10 10 10 10 10 10

Optional Modules in the Basic Sciences Field Module Code BS 113 BS 131 BS 231

Module Name Public Speaking Business Administration Economics

Level 3 3 4

4

Credit Rating 10 10 10

MODULE SPECIFICATION

Module title

English Language 1

Module code

BS 111

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

5

LEARNING OUTCOMES Knowledge and Understanding       

Audience and purpose in writing; Elements that comprise different rhetorical writing patterns; Appropriate style and register to write, revise, and edit essays; Effective planning and organisation for expository essays; The role of a thesis statement in creating unity to any piece of writing; Logically organized outlines; A wide range of vocabulary used in computing contexts.

Subject specific skills (including practical/professional skills)      

Plan and write a 700+-word expository essays using strategies such as: chronological order, cause/affect analysis, comparison/contrast, etc; Compose and discuss introductions, thesis statements, and conclusions for expository essays; Utilize correct grammar, usage, spelling, and punctuation and incorporate them in their essays; Write accurately and fluently using technical vocabulary; Edit sentences to eliminate fragments, subject-verb disagreement, dangling modifiers, faulty parallelism, and wordiness; Use effective reading skills when dealing with specialised, academic texts (identifying key points, reading for detail, interpreting data, etc.).

Cognitive skills     

Develop critical and analytical skills employed in the selection, analysis, organisation and application of relevant material to an assignment task Analyze and critique essays. Identify sound supporting details for a thesis statement. Learn skills to think and read critically and to apply these skills to write effectively. Develop competence in English language, particularly in academic computing context.

Key transferable skills      

Problem-solving, by advancing a point and defending it with appropriate supporting major and minor details; Teamwork, by working collaboratively with other students in discussing readings and writings; Autonomous learning, by being given the opportunity to evaluate themselves and others, to conduct independent research and to organise private study time; The applications of IT knowledge to essay writing, by developing competence in IT in the language learning environment, and submitting written work in acceptable form and layout; Time management and prioritisation of tasks, by writing to deadlines; Communication skills for coherent expression and transfer of knowledge. 6

INDICATIVE CONTENT         

Writing a paragraph; The process of academic writing; Outlining; Coherence; Concrete support; Writing an essay; Patterns of essay organization; Grammar Review; Selected reading texts and exercises from Infotech covering a wide variety of computing subjects.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Oshima, Alice. Writing Academic English. Third Edition. London: Longman, 1999.  Santiago & Remacha Esteras. Infotech: English for Computer Users. Third Edition. Cambridge: Cambridge UP, 2002. Supplementary reading  Langan, John. College Writing Skills with Readings. New York: McGraw Hill, 1993.  Langan, John. Ten Steps to Improving College Reading Skills. New York: McGraw Hill, 1992.

7

ASSESSMENT A: 60% B: 40%

Weighting between components A and B

ATTEMPT 1 First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination

Element weighting 100%

Component B Description of each element 1. Midterm class test 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination

Element weighting 100%

Component B Description of each element 1. Midterm class test 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

8

MODULE SPECIFICATION

Module title

Technical Report Writing

Module code

BS 114

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

9

LEARNING OUTCOMES Knowledge and understanding        

The general concepts and principles of technical writing; Qualities of good technical writing and good technical writers; The different stages of composing documents independently and collaboratively; The rules and criteria governing document design and using of illustrations; Different types and sections of technical reports; The different delivery techniques of oral reports; Different types and functions of written communication used at the workplace; Different practical strategies for employment applications.

Subject specific skills (including practical/professional skills)     

Determine the types of document suitable for internal and external use; Carry out a situational analysis to determine the topic and purpose of writing together with the receiving audience and persona of the writer; Apply the rules and criteria governing composing, gathering and evaluation of information, document design, and use of illustrations; Design and write different types of commonly used technical reports and in particular proposals, progress, recommendation and evaluation reports, and oral presentation reports; Apply appropriate strategies in job applications in writing a résumé and letter of application, and successful interview techniques.

Cognitive skills       

Conduct an audience analysis; Demonstrate skill in searching for information and evaluation of sources; Apply a range of concepts and principles associated with writing and designing technical reports, letters, memoranda, e-mails, résumé and letter of application. Distinguish the practicality of using letters, memoranda, e-mails according to the context of communication; Select the appropriate organization of the document to meet the expectation of its audience; Produce and evaluate logical argument; Apply relevant computer skills to technical writing.

Key transferable skills     

Develop problem-solving skills; Design and organize documents effectively; Present, discuss and defend ideas, concepts and views effectively through formal written documents and oral presentation; Develop an ability to work in a group and share decision making; Develop time management and organizational skills as represented in their ability to plan and implement efficient and effective modes of working. 10

INDICATIVE CONTENT 





Foundations o Composing. o Writing collaboratively. o Writing for your reader. Techniques o Gathering, evaluating, and documenting Information o Document design. o Using illustrations. Applications o Correspondence. o The strategies and communications of job hunting. o Proposals and progress reports. o Recommendation reports. o Oral reports.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Houp, K.W, Pearsall, T.E., & Tebeaux, E. (2002). Reporting Technical Information. 10th edition. Oxford: Oxford UP. Supplementary reading  Johnson-Eilola, J & Selber, S. A. (2004). Central Works in Technical Communication. Oxford: Oxford UP.  Lannon, J. (2005). Technical Communication. 10th Edition. Dartmouth: University of Massachusetts.  McMurrey, D.A. (2002). Online technical writing - online textbook [http://www.io.com/~hcexres/textbook/] [Accessed 1 May 2007], Austin, Texas.

11

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

12

MODULE SPECIFICATION

Module title

Physics

Module code

BS 141

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

13

LEARNING OUTCOMES Knowledge and understanding     

The mathematics required for the description of the physical world; The Newtonian mechanics and its application to physical systems; The quantitative description of oscillating systems and wave-motion; The Electricity, magnetism and their unification through the laws of electromagnetism; The application of electromagnetism to the description of electromagnetic waves.

Subject specific skills (including practical/professional skills)      

Use standard laboratory and observatory apparatus for physical measurements; Improve the ability to record their work concisely and precisely in the laboratory notebook, as it is done, guided by frequent feedback from teachers; Improve the ability to take reliable data, to identify the main sources of uncertainty in it, and to propagate random uncertainties into an estimate of the uncertainty on the final result; Improve the ability to condense the information contained in the record made in the laboratory notebook into a concise, but precise and complete formal report of the work in word-processed form; Increase the ability and confidence to undertake scientific investigation without the need for prescriptive instruction by completing a project of half-term duration; Apply the mathematic algebraic language to a range of physical problems.

Cognitive skills     

Apply the knowledge of physics to the solution of theoretical and practical physical problems; Apply the mathematical techniques in algebra, vectors, calculus and differential equations to the solution of physical problems; Use the computers to assist in the solution of physical problems Interpret data and make decisions taking into account experimental errors; Work with relatively little guidance.

Key transferable skills    

Communicate physical ideas in written form; Display data graphically and undertake basic word processing, including mathematical equations; Use information from a variety of sources including scientific books and the internet; Work successfully as a team member.

14

INDICATIVE CONTENT  Mechanics  Physics and measurement  Motion in one dimension  Vectors  Motion in two dimensions  Laws of motion  Circular motion and its applications  Work and energy  Kinetic and potential energy  Linear momentum and collision  Law of gravity - Waves  Oscillatory motion  Wave motion  Superposition of waves  Standing wave  Sound wave  Doppler's effect - Optics  Interference  Diffraction  Polarization - Electricity and magnetism  Electric field  Gauss' law  Kirchoff's laws  Magnetic field  Source of magnetic field and Farady's law  Electromagnetic wave TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Raymond A. Serway & Jerry S. Faughn, "College physics", Thomson-Brooks/Cole, 6th edition, 2002. Supplementary reading  Raymond A. Serway & John W. Jewett, "Physics for Scientists and Engineers ", Brooks Cole, 6 edition, 2003.

15

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

16

MODULE SPECIFICATION

Module title

Linear Algebra 1

Module code

BS 151

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

17

LEARNING OUTCOMES Knowledge and understanding    

Solution of systems of Linear equations by Gaussian and Gauss- Jordan elimination methods; Matrices; Determinants; Vectors in 2-space, 3-Space and n-Space.

Subject specific skills (including practical/professional skills)      

Provide an overview of the role of Linear Algebra in solving practical problems; Have a reasonable understanding of the definitions and terms relating to Linear Algebra introduced in the module; Have a reasonable understanding of the statements and implication of the basic theorems given in the module; Develop a critical appreciation of the central role of Linear Algebra in mathematics and in its applications; Have confidence and reasonable skill in calculating with matrices and in specific vector spaces, etc. using the theorems given by the module and with relatively little guidance; Understand the relationship between systems of linear equations, matrices, determinants, vectors, linear transformations, and Eigen values.

Cognitive skills   

Apply appropriate knowledge, analytical techniques and concepts to problems arising from both familiar (routine) and unfamiliar (novel) situations; Locate, extract and analyze data and information from a variety of different sources; Synthesis and evaluate data from multiple sources.

Key transferable skills         

Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Use appropriate packages as MATLAB, Maple, or calculator with Linear Algebra capabilities to solve simple problems just beyond the range of "hand calculation"; Work co-operatively in a group, share decision making; Act independently in planning and undertaking tasks, reflect on own learning and seek and make use of feedback; Develop problem-solving skills in relation to Linear Algebra; Acquire a reasonable facility in numerical and symbolic calculations; Acquire time-management and organizational skills, as evidence by the ability to plan and implement efficient and effective modes of working; Develop study skills in an area that lies at the heart of most advanced mathematics, statistics and applications of these areas and is therefore valuable for continuing professional development; Develop key skills in numeracy and written communication. 18

INDICATIVE CONTENT 







Systems of linear equations : - Row-reduction of linear systems - Gaussian elimination method. - Gauss-Jordan elimination method. Matrices - Matrices and matrix operations. - Rules of matrix arithmetic. - Matrices equations and Inverses. Determinants - The determinant function. - Evaluating determinants by row reduction. - Properties of determinants. - Cofactor expansion. - Cramer's rule. Vectors - Vectors in R2 and R3. - Vector arithmetic. - Dot and cross products. - Lines and planes. - Euclidean n-space vectors.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Anton, Howard "Elementary Linear Algebra with Applications" 9th Edition, New Jersey, John Wiley, 2005. Supplementary reading  Venit & Bishop, "Elementary Linear Algebra" 4th Edition Brooks Cole publishing company. 2004.

19

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

20

MODULE SPECIFICATION

Module title

Linear Algebra 2

Module code

BS 152

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

BS 151 Linear Algebra 1

Co-requisites

None

Excluded combinations

None

21

LEARNING OUTCOMES Knowledge and understanding    

Euclidean vector spaces; General vector spaces; Linear transformations; Eigen values and Eigen vectors.

Subject specific skills (including practical/professional skills)      

Provide an overview of the role of Linear Algebra in solving practical problems; Have a reasonable understanding of the definitions and terms relating to Linear Algebra introduced in the module; Have a reasonable understanding of the statements and implication of the basic theorems given in the module; Develop a critical appreciation of the central role of Linear Algebra in mathematics and in its applications; Have confidence and reasonable skill in calculating with matrices and in specific vector spaces, etc. using the theorems given by the module and with relatively little guidance; Understand the relationship between systems of linear equations, matrices, determinants, vectors, linear transformations, and Eigen values.

Cognitive skills   

Apply appropriate knowledge, analytical techniques and concepts to problems arising from both familiar (routine) and unfamiliar (novel) situations; Locate, extract and analyze data and information from a variety of different sources; Synthesis and evaluate data from multiple sources.

Key transferable skills         

Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Use appropriate packages as MATLAB, Maple, or calculator with Linear Algebra capabilities to solve simple problems just beyond the range of "hand calculation"; Work co-operatively in a group, share decision making; Act independently in planning and undertaking tasks, reflect on own learning and seek and make use of feedback; Develop problem-solving skills in relation to Linear Algebra; Acquire a reasonable facility in numerical and symbolic calculations; Acquire time-management and organizational skills, as evidence by the ability to plan and implement efficient and effective modes of working; Acquire study skills in an area that lies at the heart of most advanced mathematics, statistics and applications of these areas and is therefore valuable for continuing professional development; Develop key skills in numeracy and written communication.

22

INDICATIVE CONTENT  Euclidean vector spaces : - Linear dependence and independence. - Subspaces of Rn. - Basis and dimension.  General vector spaces - Vector spaces and subspaces - linear independence - Basis and dimension.  Linear transformations - Definition of a linear transformation. - Algebra of linear transformation. - Kernel and Image.  Eigen values and Eigenvectors - Eigen values - Eigen vectors - Diagonalization - Orthogonal Diagonalization TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Anton, Howard "Elementary Linear Algebra with Applications" 9th Edition, New Jersey, John Wiley, 2005. Supplementary reading  Venit & Bishop, "Elementary Linear Algebra" 4th Edition Brooks Cole publishing company. 2004.

23

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

24

MODULE SPECIFICATION

Module title

Mathematical Analysis 1

Module code

BS 153

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

25

LEARNING OUTCOMES Knowledge and understanding   

The basic notion of a function; The concepts and processes of differential calculus of functions of one variable and some of its applications; The concepts and processes of integral calculus of functions of one variable and some of its applications.

Subject specific skills (including practical/professional skills)        

Manipulation of expressions involving trigonometric quantities using trigonometric identities; Use of tables to obtain derivatives of standard functions; Application of the product, quotient and chain rules to determine the derivative of a function; Determining the first and second derivatives of a function represented in parametric form; Locating the points of the maximum, minimum values and inflection of a function; Using tables to obtain the indefinite integral of standard functions; Performing integration by substitution, by parts and by partial fractions; Evaluating definite integrals and its application to determine the area contained within a region bounded by the curve of a standard function.

Cognitive skills      

Demonstrate skill in calculation and manipulation of the material written within the course; Apply a range of concepts and principles in various contexts; Use logical argument; Demonstrate skill in solving calculus problems by various appropriate methods; Use relative computer skills; Work with relatively little guidance.

Key transferable skills     

Develop problem – solving skills in relation to calculus; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Acquire a reasonable facility in numerical and symbolic calculation; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

26

INDICATIVE CONTENT 



Differentiation - Differentiation of algebraic functions of one variable - Chain – Rule. - Higher order derivatives. - Implicit differentiation. - Differentiation of trigonometric functions. - Differentiation of inverse - trigonometric functions. - Differentiation of hyperbolic functions. - Differentiation of inverse hyperbolic functions. - Differentiation of exponential and logarithmic functions. - First and second derivatives of functions expressed parametrically. - Tangents and normals to a curve. - Maximum, Minimum and inflection points of a curve. Integration - Integration as the inverse operation of differentiation - Integration of polynomials , trigonometric and exponential functions. - Integration of products and fractions. - Integration by substitution. - Integration by parts. - Integration by partial fractions. - Definite integrals: Calculation of area under a curve and solids of revolution.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Frank Ayres, "Calculus", Schaum's outline series, Schaum Publishing company 1972  E.W. Swokoski, M. Olinick, D. Pence and J.A. Cole," Calculus," PWS Publishing Company, Boston, 6th Ed., 1994. Supplementary reading  R.Finney and G. Thomas, "Calculus," Addison Wesley Publishing Company, 2nd Ed, 1994  E.Kreyzig, "Advanced Engineering Mathematics," John Wiley and Sons Co., 7th Ed, 1993. 27

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

28

MODULE SPECIFICATION

Module title

Mathematical Analysis 2

Module code

BS 154

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

BS 153 Mathematical Analysis 1

Co-requisites

None

Excluded combinations

None

29

LEARNING OUTCOMES Knowledge and understanding     

The concepts of differentiation of functions of two or more variables; First order ordinary differential equations and their methods of solution; Plane and space vectors and their algebra; Series and sequences, the different tests for their convergence and some of their applications; Laplace transform and its use to solve first and second order ordinary differential equations.

Subject specific skills (including practical/professional skills)       

Determine the partial derivatives of functions of two or more variables; Find the total derivative in terms of partial derivatives using the chain rule; Use the appropriate method to solve first order ordinary differential equations, separable, homogeneous, and exact; Perform different mathematical and algebraic operations on plane and space vectors; Identify different types of infinite series and sequences and determine whether they are convergent or divergent using the appropriate test; Express functions as a power series and to determine Maclurin's and Taylor's series of a function; Use of Laplace transformation for solving first and second-order ordinary differential equations.

Cognitive skills      

Demonstrate skill in calculation and manipulation of the material written within the course; Apply a range of concepts and principles in various contexts; Use logical argument; Demonstrate skill in solving calculus problems by various appropriate methods; Use relative computer skills; Work with relatively little guidance.

Key transferable skills     

Develop problem – solving skills in relation to calculus; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Acquire a reasonable facility in numerical and symbolic calculation; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

30

INDICATIVE CONTENT 







Partial differentiation - Partial derivatives - Total differential , total derivatives Differential equations - Definition, classification, terminology, techniques of solution - First order separable equations. - First order homogeneous equations. - First order exact equations. Vectors and their algebra - Plane and space vectors. - Addition, subtraction, quotient, multiplication, and differentiation. - Dot products and vector products. Infinite series - Definitions, terminology, notation , classification - Convergence and divergence of positive – term Series - Laplace Transformation:  Laplace Transformation of functions, derivatives, integrals  Differentiation and integration of transforms.  Solution of first & second order ordinary differential equations.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Frank Ayres, "Calculus," Schaum's outline series, Schaum Publishing company 1972  E.W. Swokoski, M. Olinick, D. Pence and J.A. Cole," Calculus," PWS Publishing Company, Boston, 6th Ed., 1994. Supplementary reading  R.Finney and G. Thomas, "Calculus," Addison Wesley Publishing Company, 2nd Ed, 1994  E.Kreyzig, "Advanced Engineering Mathematics," John Wiley and Sons Co., 7th Ed, 1993.

31

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

32

MODULE SPECIFICATION

Module title

Applied Electronics

Module code

BS 161

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

BS 153 BS 141

Co-requisites

None

Excluded combinations

None

33

Mathematical Analysis 1 Physics

LEARNING OUTCOMES Knowledge and understanding     

The components of and laws governing DC circuit theory; The components of and laws governing AC circuit theory; The characteristics and construction of simple frequency selective circuits; The characteristics and principles of operation and applications of diodes; The characteristics and principles of operation and applications of bipolar Junction Transistors (BJT) and field effect Transistors (FET).

Subject specific skills (including practical/professional skills)        

Implementing Kirchhoff's voltage and current Laws and Ohm's law in simple DC and AC circuit analysis; Using the node-voltage and mesh-current methods for solving the appropriate class of problems of DC and AC circuits; Using Thevenin's theorem and maximum-power-transfer theorem for solving the appropriate class of problems of DC and AC circuits; Using the operational amplifier (OPAmp) as the basic building block for various applications and deriving the necessary conditions for each of these applications; Using simple frequency selective circuits as Low-Pass, High-pass, and Band-Reject filters and deriving the necessary working conditions; Implementing p-n junction diodes for various appropriate applications; Implementing the appropriate connection of Bipolar Junction Transistor (BJT) amplifier for a specific application; Implementing the appropriate connection of Field –Effect Transistor (FET) amplifier for a specific application.

Cognitive skills      

Demonstrate skill in the analysis and solution of problems associated with DC and AC circuit theory. Apply a range of concepts and principles in various contexts. Use logical argument. Demonstrate skill in selecting the most appropriate methods of solution for different types and categories of problems encountered in this course. Use relative computer skills Work with relatively little guidance.

Key transferable skills     

Develop problem – solving skills in relation to both DC and AC circuits; Present , discuss and defend ideas , concepts and views effectively through formal and informal written language; Acquire a reasonable facility in numerical and symbolic calculations; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working. 34

INDICATIVE CONTENT           

SI System of units; Simple resistive circuits; Techniques of circuit Analysis; Operational amplifiers; Alternating current circuits; Sinusoidal steady – state analysis; Introduction to frequency – selective circuits; Diodes and applications; Bipolar junction transistor; Bipolar junction transistor amplifiers; Field – effect transistors.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  J. Nilson & S. Riedel, " Electric Circuits" , New Jersey, Prentice Hall, 2005  N.P. Cook, "Electronics: A Complete Course", New Jersey, Pearson Education Inc, 2004. Supplementary reading  Albert P.Malvino, "Electronic Principles", McGraw-Hill Book Company, 1993  A.S Sedra.and K.C.Smith," Microelectronic Circuits". Oxford University Press, 1998.

35

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 3. A 3-hour unseen examination. 4. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 4. Midterm class test. 5. Assignments and quizzes 6. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 3. A 3-hour unseen examination. 4. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 4. Midterm class test. 5. Assignments and quizzes 6. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

36

MODULE SPECIFICATION

Module title

Logic and Discrete Mathematics

Module code

BS 251

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

37

LEARNING OUTCOMES Knowledge and understanding     

Understand the meaning of mathematical definitions of sets and perform set operations; Symbolise simple verbal arguments and test their validity by truth-tables and by resolution; Use truth-tables to test for tautologies, contradictions and for logical equivalence; Understand the meaning of mathematical definition of relations and to determine what relations are equivalence relations; Understand the meaning of the mathematical definition of functions.

Subject specific skills (including practical/professional skills)      

Find the equivalence relations among the relations; Understand the functions definitions and how to get the inverse functions; Perform some steps in mathematical inductions to prove formulas; Identify the congruence and how to make some operations in congruence; Use the Euclidean Algorithm, and perform the operations related to it; Define graphs, and how to write the correct paths and circuits inside the graphs.

Cognitive skills      

Demonstrate skill in set operations; Apply a range of concepts and principles in various contexts; Use logical argument; Demonstrate skill in solving problems by various appropriate methods. Use relative computer skills; Work with relatively little guidance.

Key transferable skills     

Develop problem – solving skills in relation to discrete mathematics; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Acquire a reasonable facility in numerical and symbolic calculations; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

38

INDICATIVE CONTENT 





Sets Relations, and Functions - Set operations - Equivalence Relations - Functions. - Mathematical Induction Coding Theory - Congruence. - The Euclidean Algorithm Graphs - Graphs and their representations - Paths and Circuits

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  John Dossey, Albert D. Otto, Lawrence E. Spence, and Charles Vanden Eynden, " Discrete Mathematics," Pearson Addison Wesley, 2006 Supplementary reading  N CH SN Iyengar , VM Chandraselaran, KA Arunachalam, " Discrete Mathematics," Vikas Publishing House Pvt Ltd.2004

39

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

40

MODULE SPECIFICATION

Module title

Numerical Analysis

Module code

BS 252

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

BS 154 Mathematical analysis 2 CS 112 Programming Languages

Co-requisites

None

Excluded combinations

None

41

LEARNING OUTCOMES Knowledge and understanding     

The meaning of mathematical definitions of solving equations; Solving equations using the Bisection Method; Using Fixed Point Iteration to solve equations; Understanding the meaning of mathematical algorithm of Newton's method and using it to solve equations; Understanding the different methods for solving system of equations.

Subject specific skills (including practical/professional skills)      

Find the suitable method for solving equations; Understand the Bisection and Newton' s methods and using them to solve the equations; Perform some steps in fixed point iteration to solve equations; Use Gaussian Elimination to solve system of equations; Use the L-U Factorization Algorithm, and perform the steps needed to solve system of equations; Use Conjugate Gradient Method to solve system of equations.

Cognitive skills      

Demonstrate skill in numerical analysis; Apply a range of concepts and principles in various contexts; Use logical argument; Demonstrate skill in solving problems by various appropriate methods. Use relative computer skills; Work with relatively little guidance.

Key transferable skills     

Develop problem – solving skills in relation to numerical analysis; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Acquire a reasonable facility in numerical and symbolic calculations; Work co-operatively in a group and share decision making; Develop time-management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

42

INDICATIVE CONTENT 



Solving Equations - The Bisection Method. - Fixed Point Iteration. - Newton's Method. System of Equations - Gaussian Elimination. - The L-U Factorization. - The PA = LU Factorization - Iterative Methods - Conjugate Gradient Method

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Timothy Sauer, " Numerical Analysis," Pearson, Addison Wesley, 2006 Supplementary reading  Jeffery J. Leader, " Numerical Analysis and Scientific Computation," Pearson Education .Inc.2004

43

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

44

MODULE SPECIFICATION

Module title

Probability and Statistics

Module code

BS 253

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

BS 151 Linear Algebra 1 BS 153 Mathematical analysis 1

Co-requisites

None

Excluded combinations

None

45

LEARNING OUTCOMES Knowledge and understanding     

Understanding the meaning of mathematical definitions of sample space, events; Counting sample points; Calculating the probability of the sample points, and the events, and how to use the additive rule; Evaluating the discrete and continuous probability distributions; Understanding the meaning of mean and variance of random variables.

Subject specific skills (including practical/professional skills)      

Understand the concept of sample space and events; Count sample points and events, and find their probabilities; Perform some steps using additive rule; Identify the concept of a random variable; Calculate discrete and continuous probability distributions; Define and calculate mean, variance, and covariance of random variables.

Cognitive skills      

Demonstrate skill in probability operations; Apply a range of concepts and principles in various contexts; Use logical argument; Demonstrate skill in solving problems by various appropriate methods. Use relative computer skills; Work with relatively little guidance.

Key transferable skills     

Develop problem – solving skills in relation to probability operations; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Acquire a reasonable facility in numerical and symbolic calculations; Work co-operatively in a group and share decision making; Develop time-management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

46

INDICATIVE CONTENT 





Probability - Sample space. - Events. - Counting sample points. - Probability of an Event, and additive rule Random Variables and probability Distributions - Concept of a random variable. - Discrete and continuous probability Distributions. Mathematical Expectation - Means of a random variable - Variance and Covariance of Random Variables.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  RE. Walpole, RH Myers, S.L. Myers, and K.Ye, " Probability & Statistics for Engineers & Scientists," Pearson International Edition, 2006 Supplementary reading  Douglas C. Montgowery, George C. Ringer , " Applied Statistics and Probability for Engineers," John Wiley & Sons , Inc 1999

47

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

48

MODULE SPECIFICATION

Module title

Public Speaking

Module code

BS 113

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

49

LEARNING OUTCOMES Knowledge and understanding        

The communication process of public speaking; Means to conquer the stage fright; Basic procedures and constructions of public speaking; Audience centeredness and audience adaptation; Different methods of delivering a speech; The aural and visual dimensions of presentation; The elements of effective listening; Ethical decisions underlying public speaking.

Subject specific skills (including practical/professional skills)  

     

Speak with confidence in a public speaking situation; Demonstrate the skills necessary in preparing effective informative and persuasive speeches, including choosing a subject, brainstorming, evaluating the audience and the occasion, researching material, assembling evidence, organizing arguments, writing an outline, styling the presentation, and writing the final work; Perform audience analysis using interviewing and questionnaires; Formulate the general and specific purpose of a speech; Make a preparation outline and a speech outline with delivery cues; Use various tactics in introducing, supporting, and concluding a speech; Demonstrate delivery techniques in making effective oral presentations; Design visual aids appropriate to the message.

Cognitive skills        

Recognize the values of public speaking in academic contexts, in the workplace and in social life Explain and recognize the elements of effective communication in public speaking situations Name and recognize effective listening skills, and to use them in performance as both speaker and audience Recognise the importance of context and environment on speech and listening situations Identify the major demographic traits of the audience and situational traits of the occasion. Identify and control feelings of communication apprehension; and develop confidence in making oral presentations to audiences Critically examine ideas and information represented in oral language and nonverbal behaviour; hence, analyze and critique their own presentations and their peers’ Select the appropriate visual aids for a better delivery of the message.

50

Key transferable skills 

     

Communication skills for coherent expression and transfer of knowledge, with particular reference to public speaking, by confidently delivering oral messages suitable to the topic, purpose, and audience within a public setting and effectively listening and evaluating when receiving information. Problem-solving, by advancing a point and defending it with appropriate supporting evidence. Teamwork, by working collaboratively with other students in brainstorming and discussing speech ideas. Autonomous learning, by being given the opportunity to evaluate themselves and others, to conduct independent research and to organise private study time The applications of IT knowledge to public speaking, by employing their expertise in preparing and using visual aids and submitting their work in acceptable form and layout Time management and prioritisation of tasks, by working for deadlines. Research skills, by gathering information for their speeches from different resources such as the library, the Internet, interviews, etc.

INDICATIVE CONTENT         

Speaking in public; Ethics and public speaking; Listening; Analysing the audience; Speech organisation; Delivery; Visual aids; Speaking to inform; Speaking to persuade.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Lucas, S. E. (2006). The Art of Public Speaking (9th ed.). Boston: McGraw Hill Supplementary reading  Makay, J. H. (2000). Public Speaking: Theory into Practice. 4th Edition. New York: Kendall Hunt  Hamilton, C (2006). Essentials of Public Speaking. 3rd Edition. Wadsworth 51

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

52

MODULE SPECIFICATION

Module title

Business Administration

Module code

BS 131

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

53

LEARNING OUTCOMES Knowledge and understanding        

The highly dynamic environment of business; Competition in the global markets; The business mission statement and the business strategy; The internal and external environments of the business organization; The different functions performed by business organizations to achieve their objectives; Integration between the different organizational functions; The various challenges faced by modern business organizations in their operations; Human resources as the most valuable asset to the organization.

Subject specific skills (including practical/professional skills)     

Describe the developing trends facing the business environment; Evaluate competition in global markets; Analyze the challenge of starting a small business; Apply information technology to business situations; Assess and evaluate the resources of a business.

Cognitive skills   

Apply appropriate knowledge, analytical techniques and concepts to problems and issues arising from both familiar (routine) and unfamiliar (novel) situations Locate, extract and analyze data and information from a variety of different sources Synthesise and evaluate data and information from multiple sources

Key transferable skills     

Present written information in a report format with clarity and logical coherence Present, discuss and defend ideas, concepts and views effectively through formal and informal written language Use appropriate IT packages to aid efficient searching, communication and presentation of information Work co-operatively in a group, share decision making and negotiate' with others Act independently in planning and undertaking tasks, reflect on own learning and seek and make use of feedback

54

INDICATIVE CONTENT        

Exploring trends in the dynamic business environment; Competing in global markets; Entrepreneurship and the challenge of starting a small business; Production, products and services; Marketing; Information technology and business; Financial resources; Human resources management.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Madura, Jeff 2003. Introduction to Business. 3rd edition. South-Western College Pub Supplementary reading  Gasper, Julian E., Introduction to Business, International edition  Griffin, Ricky. Management. Boston: Houghton Mifflin, 2002

55

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

56

MODULE SPECIFICATION

Module title

Economics

Module code

BS 231

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Basic Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

57

LEARNING OUTCOMES Knowledge and understanding   

The main ways in which economists think about problems; The basic principles of economics; The main debates about economic problems and policies.

Subject specific skills (including practical/professional skills)     

Follow and use economic analysis to be able to present economic arguments and ideas; Describe economic theories; Explain the nature of supply and demand; Describe and analyse economics costs; Describe and explain the nature of monetary policy.

Cognitive skills   

Apply appropriate knowledge, analytical techniques and concepts to problems and issues arising from both familiar (routine) and unfamiliar (novel) situations Locate, extract and analyze data and information from a variety of different sources Synthesise and evaluate data and information from multiple sources

Key transferable skills     

Present written information in a report format with clarity and logical coherence Present, discuss and defend ideas, concepts and views effectively through formal and informal written language Use appropriate IT packages to aid efficient searching, communication and presentation of information Work co-operatively in a group, share decision making and negotiate' with others Act independently in planning and undertaking tasks, reflect on own learning and seek and make use of feedback

58

INDICATIVE CONTENT          

The fundamentals of economics; Scarcity and choice - the economics problem; Markets and government in a modern economy; Basic elements of supply and demand; Applications of supply and demand; Demand and consumer behaviour; Analysis of cost; Fiscal policy; Money and banking systems; Monetary policy and the national economy.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Mankiw, N.Gregory 2002. Principles of Economics, 2nd edition Thomson SouthWestern Supplementary reading  Samuelson, Paul A. , Nordhaus, William D.2001. Economics 17th edition McGraw-Hill Irwin

59

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

60

Section 3 Modules in the Computer Science Field

61

Compulsory Modules in the Computer Sciences Field Module Code CS 111 CS 131 CS 112 CS 211 CS 212 CS 213 CS 214 CS 221 CS 241 CS 242 CS 311 CS 312 CS 321 CS 331 CS 351 CS 362 CS 363 CS 412 CS 413 CS 421 CS 422 CS 431 CS 432 CS 461 CS 463 CS 492

Module Name Introduction to Computers ECDL Programming Languages Computer Programming Fundamentals 1 Computer Programming Fundamentals 2 Object Oriented Programming 1 Object Oriented Programming 2 Digital Hardware Data Communication and Protocols Computer Networks 1 Logic Programming Algorithms and Data Structures Computer Architecture Software Engineering 1 Operating Systems Internet Technologies Communication Technology Computer Security Computer Graphics Microsystems Distributed Systems Artificial Intelligence Software Engineering 2 Digital Signal Processing Pattern Recognition Project

62

Level 3 3 3

Credit Rating 10 10 10

4

10

4 4 4 4 4 4 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6

10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 20

Optional Modules in the Computer Sciences Field Module Code CS 313 CS 314 CS 332 CS 341 CS 342 CS 414 CS 415 CS 462 CS 471

Module Name Neural Networks Assembly Language Knowledge-Based Systems Computer Networks 2 Information and Computer Network Security Multi-Agent Systems Natural Language Processing Image Processing Multimedia

63

Level 5 5 5 5

Credit Rating 10 10 10 10

5

10

6 6 6 6

10 10 10 10

MODULE SPECIFICATION

Module title

Introduction to Computers

Module code

CS 111

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

64

LEARNING OUTCOMES Knowledge and understanding      

The role of the principal functional components of a computer; The difference data versus information; The information system concepts including input, processing, and output; The purpose and major characteristics of the major hardware components of an computer-information system; The concepts of systems software and application software; The information system components including hardware, software, databases and telecommunications.

Subject specific skills (including practical/professional skills)    

Select computer hardware; Select software; Select software; Explain alternative system building approaches such as prototyping, end user development, software packages, and outsourcing.

Cognitive skills      

Understand computer related tools and skills; Describe a variety of data representation systems, be able to manipulate numbers in these representations, and relate these representations to the storage of numbers and other data; Demonstrate understanding of the new role and widening scope of information systems in organizations; Explain the strategic role information systems play in organizations; Identify and define the major types of information systems including transaction processing systems, management information systems, decision support systems, expert systems and Enterprise Resource Planning (ERP); Describe the ethical and social impact of information systems.

Key transferable skills   



Gain a variety of skills that span different aspects of working environments; Demonstrate understanding of the evolution of Computers; Use common personal productivity tools; Use the Internet to conduct research, find information, and communicate.

65

INDICATIVE CONTENT            

Computers and computing fundamentals; Interacting with computers; Output devices; Processing data; Storing information in a computer; The operating system and user interface; Networks and data communications; The internet and online resources; Computer graphics and design; The multi-media; Development of information systems; Living with computers.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Peter Norton , "Introduction to Computers" 4th Edition, California McGraw-Hill, 2001 Supplementary reading  Noel Kalicharan , "An Introduction to Computer Studies," Cambridge University Press, 1996  Jeffrey Frates, William Moldrup , "Introduction to the Computer: An Integrated approach" 2nd Edition, Prentice Hall, 1984

66

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

67

MODULE SPECIFICATION

Module title

European Computer Driving License- ECDL

Module code

CS 131

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

68

LEARNING OUTCOMES Knowledge and understanding          

A range of basic uses of ICT both in an academic context and in more general usage; The principles of the way in which a personal computer works; Sharing information seamlessly between Office XP applications and colleagues using Share Point™ team services; Importing real-time data into spreadsheets and Web pages; Creating professional-quality print and online publications; Building own databases, and using powerful data-analysis techniques; Delivering compelling PowerPoint presentations at work or through the Web; Using Outlook to master own schedule and e-mail communications; Constructing and managing a Web site with advanced features; Developing custom solutions using macros and Microsoft Visual Basic® for Applications.

Subject specific skills (including practical/professional skills)   

Perform basic skills in a range of software including: the Windows operating system, a word processor, spreadsheet, database, web browser, email and presentation graphics software; Perform some more advanced skills in each of the applications listed in the previous point; Manage their own learning and development including the management of resources and time.

Cognitive skills   

Identify which software application should be used to address a particular problem; Identify some limitations of basic ICT skills and recognize the need to develop skills further; Identify some of the ways in which e-Learning can supplement more traditional learning and teaching strategies.

Key transferable skills         

Gain skills that span different aspects of working environments; Produce advanced word processing outputs, illustrating sophisticated typographical formatting and layout presentations, including tables, forms or graphs; Use tools such as macros and carry out more advanced mail merge operations; Carry out advanced and presentation operations on charts Use functions associated with logical, statistical or mathematical. Use functions associated with logic, statistics or mathematics; Understand the variety of ways that data can be related and organised; Create advanced charts/graphs and be able to enhance the presentation by using drawing and image tools to modify drawn objects and images; Use common personal productivity tools. 69

INDICATIVE CONTENT          

Introduction to Office XP; Common Office XP Techniques; MS Word; Excel; PowerPoint; Outlook; FrontPage; Access; Publisher; Supercharging Office XP with Macros and VBA.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Michael Halvorson and Michael J. Young, “Microsoft® Office XP Inside Out”, MSPress, 2001 Supplementary reading  Carol Brown and Resources Online, “Microsoft® Office XP Plain & Simple" 2001, MS-Press  Perspection, Inc., Online Training Solutions, Inc., Curtis Frye, Kri, “Microsoft® Office XP Step By Step” 2001, MS-Press  Wallace Wang, Wang, Wally Wang "Microsoft Office XP for Dummies" ISBN: 076450830X , May 2001

70

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

71

MODULE SPECIFICATION

Module title

Programming Languages

Module code

CS 112

Level

3

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

CS 111 Introduction to Computers

Co-requisites

None

Excluded combinations

None

72

LEARNING OUTCOMES Knowledge and understanding      

The syntax and structure of C and C++ programs; The definition of variables and functions definitions and callings; Pointers and dynamic allocation of variables; Structure and union; Files and I/O streamers; How C++ is implemented on the host architecture.

Subject specific skills (including practical/professional skills)   

Apply basic algorithmic patterns to solve problems; Write and debug programs in using appropriate tools; Use 3rd party libraries of functions or classes.

Cognitive skills   

Write programs using console; Write programs that do I/O; Design C and C++ programs to meet requirements expressed in English.

Key transferable skills    

Develop fundamental skills such as problem solving and abstract reasoning through computer programming; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

73

INDICATIVE CONTENT            

Computers and computing fundamentals; Program structures; Variables and arithmetic operations; Basic input/output; Decision making; Iteration; Functions; One-dimensional numeric arrays; Multi-dimensional numeric arrays; Pointer Variables; Character Arrays; Structure and union type and dynamic data structure.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  D' Orazio, Timothly, "Programming in C++: Lessons and Applications" Boston, McGraw-Hill, 2004 Supplementary reading  Hanly, Jeri & Elliot Koffman; "Problem Solving & Program Design in C " Massachausettes, Addison-Wesley, 1996  Steve Oualline “Practical C++ Programming” 2nd Edition, O'Reilly & Associates, 2003

74

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

75

MODULE SPECIFICATION

Module title

Computer Programming Fundamentals 1

Module code

CS 211

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

BS 251 Logics & Discrete Mathematics

Co-requisites

None

Excluded combinations

None

76

LEARNING OUTCOMES Knowledge and understanding     

Know the meaning of mathematical definitions of number theory and perform some operations; Symbolise simple verbal arguments and test their validity by reasoning and by resolution; Use greatest common divisors to solve linear equations; Understand the mathematical definition of concurrences and the Fermat' little theorem; Understand the mathematical definition of Euler' Phi function, and the Chinese Remainder Theorem.

Subject specific skills (including practical/professional skills)     



Find the Pythagorean Triples, and analyse their relation to the greatest common divisors; Understand the definition of the greatest common divisor and how to use it to solve linear equations; Understand the mathematical meaning of the Fundamental Theorem of Arithmetic; Identify and carry out operations in congruence. Use Fermat's little Theorem ,and Euler's Phi Function; Use the Chinese Remainder Theorem.

Cognitive skills      

Demonstrate skills in Number Theory; Apply a range of concepts and principles in various contexts; Use logical argument; Demonstrate skills in solving problems by various appropriate methods; Use relative computer skills; Work with relatively little guidance.

Key transferable skills     

Develop problem – solving skills in relation to Number theory; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Acquire a reasonable facility in numerical and symbolic calculations; Work co-operatively in a group and share decision making; Develop time-management and organisational skills as evidenced by the ability to plan and implement efficient and effective modes of working.

77

INDICATIVE CONTENT        

Pythagorean Triples; Divisibility and the Greatest Common Divisor; Linear Equations and the Greatest Common Divisor; Factorization and the Fundamental Theorem of Arithmetic; Congruences; Congruences, Powers, and Fermat's Little Theorem; Congruences, Powers, and Euler's Formula; Euler's Phi Function and the Chinese Remainder Theorem.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Joseph H. Silverman, " A Friendly Introduction to Number Theory", Pearson Education International. 2006 Supplementary reading  J.W.Bruce, P.J. Giblin, P.J Rippon, " Micro computers and Mathematics" Cambridge University Press.1990

78

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

79

MODULE SPECIFICATION

Module title

Computer Programming Fundamentals 2

Module code

CS 212

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

CS 211 Computer Programming Fundamentals 1

Co-requisites

None

Excluded combinations

None

80

LEARNING OUTCOMES Knowledge and understanding          

Proof Techniques - Constriction, Induction, and Contradiction; Decision versus Optimization problems; Language recognition problems; Language hierarchy (Regular, Context Free, Recursive languages); Computational Models (Finite State, Push Down, and Turing Machines); Undesirability; Complexity Classes (P, NP, and NP-Complete); Declarative versus procedural languages; Concurrency and concurrency control; Mobile agent computing.

Subject specific skills (including practical/professional skills)        

Construct a lexical analyser; Construct a syntax analyser; Construct a Compiler; Design a controller using finite state machines; Judge on a language class; Analyze the complexity of existing algorithms; Design a new algorithm; Control concurrency in distributed systems.

Cognitive skills    

Prove using induction, construction, and contradiction; Disprove using counter examples; Deduce if a problem is solvable or not; Design the solution algorithm and analyse both time and space complexity of the algorithm.

Key transferable skills     

Develop problem – solving skills in relation to programming fundamentals; Develop presentation skills for clear and precise expression. Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Work co-operatively in a group and share decision making; Develop time management and organisational skills as evidenced by the ability to plan and implement efficient and effective modes of working.

81

INDICATIVE CONTENT                

Proof Techniques (Constriction, Induction, and Contradiction); Deterministic Finite State Automata DFA; Nondeterministic FA (NFA); NFA and DFA equivalence; Regular expressions RE; Equivalence of RE and NFA; Closure of Regular languages under union, Keene star; Pumping Lemma for regular languages; Context free grammar and languages(CFG) and (CFL); Push Down Automata (PDA); Closure of CFL under languages operations; The Pumping Lemma for CFL; The Recursive languages and Turing Machines; The Halting problem of Turing machine; Un decidability and Post Correspondence Problem; Introduction to Complexity Theory.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Introduction to the Theory of Computation, Michel Sipser 2nd Ed., PWS Publishing Company, 2005 Supplementary reading  Hopcroft, Ullman, and Motwani. Theory, Languages, and Computation, 2nd edition, Addison-Wesley, 2003.  Daniel I. A. Cohen. Introduction to Computer Theory, 2nd Edition, ,Wiley,1996  Harry R. Lewis, Christos H. Papadimitriou. Elements of the Theory of Computation (2nd. ed.), Prentice-Hall, August 1997.  J. Hopcroft, R. Motwani and J. Ullman. Automata Theory, Languages, and Computation (Third Edition), Addison Wesley, 2007.

82

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

83

MODULE SPECIFICATION

Module title

Object Oriented Programming 1

Module code

CS 213

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

CS 112 Programming Languages

Co-requisites

None

Excluded combinations

None

84

LEARNING OUTCOMES Knowledge and understanding    

The basics of object oriented programming. Concepts of encapsulation, abstraction, inheritance, polymorphism as they are realized in an- Object-oriented approach to system construction. The implementation of these techniques and concepts in a suitable programming language. The advantages of the OO paradigm for software engineering.

Subject specific skills (including practical/professional skills)   

Breakdown simple programming goals into object- oriented components; Propose and evaluate different designs for solving problems using knowledge of fundamental programming techniques; Use the facilities available in the C++, and according to the relative space and time efficiency of the proposed solutions, implement the solution using C++, test and evaluate the finished code.

Cognitive skills  

Demonstrate a basic understanding of the key object-oriented concepts of encapsulation, inheritance and polymorphism and explain how these are implemented in the chosen language. Match programming language constructs to the needs of a programming problem of moderate complexity involving collections of objects.

Key transferable skills     

Develop problem – solving skills in relation to object oriented programming. Present, discuss and defend ideas, concepts and views effectively through formal and informal written language. Acquire a reasonable facility in numerical and symbolic calculations. Work co-operatively in a group and share decision making. Develop time management and organisational skills as evidenced by the ability to plan and implement efficient and effective modes of working.

85

INDICATIVE CONTENT            

Introduction to programming; Simple overview of program development, stepwise refinement and pseudo code, overview of program structure; Variable declaration and basic data types. Simplified input and output in C++ Operators: precedence and associatively, type conversion, expressions, simple statements, relational and logical operators; Files I/O; Flow of control: compound statements, if, loops, switches; Basic data types: strings arrays, single dimension arrays and collection classes; Methods; Class definition; Exception handling; Abstraction, inheritance and interfaces; Accessing and updating data in files; Design of programs for sequential data processing.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  D’orazio, “Programming in C++ Lessons and applications,” International edition, MC Graw Hill Supplementary reading  Timothy Budd, “Data Structures in C++, using the Standard Template Library,” Addison,Wesley, 1998  Bjarne Stroustrup, “The C++ Programming Language”, 3rd edition, AddisonWesley,1997

86

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 5. A 3-hour unseen examination. 6. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 7. Midterm class test. 8. Assignments and quizzes 9. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 5. A 3-hour unseen examination. 6. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 7. Midterm class test. 8. Assignments and quizzes 9. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

87

MODULE SPECIFICATION

Module title

Object Oriented Programming 2

Module code

CS 214

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

CS 213

Co-requisites

None

Excluded combinations

None

88

Object Oriented Programming 1

LEARNING OUTCOMES Knowledge and understanding          

C# language and Net platform Variables and program structures in C# Definition of class and how to control its accessibility Using the constructor and static methods How inheritance works in C# Overloading, sealed, abstract classes and interfaces boxing and un-boxing techniques Creating components in classes such as properties, delegates and events Working with windows applications in C# Managing data through working with data binding & data sets Building Web applications in C# environment

Subject specific skills (including practical/professional skills)      

Use object oriented techniques in building program application Be aware of C# programming environment Understand advanced object oriented techniques in C# Build windows applications in C# Access SQL server databases using C# Be aware of building web applications in C#

Cognitive skills    

Investigate principles of modelling underlying advanced object oriented programming Understand C# as a specific language the implements these principles Use an IDE of C# to develop object oriented programs Develop and test programs in C#

Key transferable skills     

Develop problem – solving skills in relation to object oriented programming Present, discuss and defend ideas, concepts and views effectively through formal and informal written language Acquire a reasonable facility in numerical and symbolic calculations Work co-operatively in a group and share decision making Develop time management and organisational skills as evidenced by the ability to plan and implement efficient and effective modes of working.

89

INDICATIVE CONTENT               



Welcome to C# Working with variables ,Operators, and Expressions Writing Methods and applying scope Creating and Managing classes and objects Understanding values and references Understanding ref and out keywords, box and unbox a value Creating Value Types with Enumerations and Structs Working with Inheritance Implementing Properties to Access Attributes Delegates and Events Operator Overloading Introducing Windows Forms Working with Menus and Dialog Boxes Using a Database Working with Data Binding and Data Sets Introducing ASP.NET

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  H-M. Deitel & P.J. Deitel, "Visual C# 2005: How to program", Pearson Education Inc, 2nd Ed., 2006. Supplementary reading  Christian Nagel, Bill Evjen, Jay Glynn, and Morgan Skinner, " Professional C# 2005," Wrox Professional Guides .2005  John Sharp, " Microsoft Visual C# 2005 Step by Step, " Microsoft Corp.2005  Peter Wright, " Beginning Visual C# 2005 Express Edition": From Novice to Professional.Apress.2006

90

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 7. A 3-hour unseen examination. 8. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 10. Midterm class test. 11. Assignments and quizzes 12. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 7. A 3-hour unseen examination. 8. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 10. Midterm class test. 11. Assignments and quizzes 12. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

91

MODULE SPECIFICATION

Module title

Digital Hardware

Module code

CS 221

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

BS 161 Applied Electronics

Co-requisites

None

Excluded combinations

None

92

LEARNING OUTCOMES Knowledge and understanding        

Transfer any problem of digital nature and sequential behaviour to a state diagram description in case of small numbers of states problems Achieve the minimum-cost of circuit realisation Know how to get the state table for a given sequential circuit Know how to estimate the size of the problem to choose either flip-flop or modular realisation circuitry Make a proper layout design of the data path that fits the allocated problem requirements Design the control system of the given problem which secures the correct sequence of output signals which control the transfer of data among data path registers Know how to verify the overall design correctness Design the memory circuit required to transfer data from and to data path under control of control unit and the associated address and data registers for executing microinstructions if it exist

Subject specific skills (including practical/professional skills)   

Design a special –purpose computing system satisfying special requirements with cheaper price than normal computers Realize a digital system operating in real-time which have computational time much less than that of normal PC Modify existing digital system to achieve either better performance or special application

Cognitive skills    

Develop a special imagination for creating solutions for surrounding problems using the knowledge absorbed in this course Analyze any given system and extract the bugs in this system Create of revolutionary attempts to solving difficult sophisticated problems by logic approaches gained in course, giving rise up to simple and cheep solutions Create one's own measuring procedure and self-correction means to proposed systems

Key transferable skills     

Develop problem – solving skills in relation to digital hardware Present, discuss and defend ideas, concepts and views effectively through formal and informal written language Acquire a reasonable facility in numerical and symbolic calculations Work co-operatively in a group and share decision making Develop time management and organisational skills as evidenced by the ability to plan and implement efficient and effective modes of working

93

INDICATIVE CONTENT     



Introduction Logic functions, representation, and realization Logic function minimization Combinational logic modules Sequential logic circuit elements Sequential Logic circuit modules

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Mano M M, and Kime, C R,"Logic and Computer Design Fundamentals 2nd ed, Englewood Cliffs, N J Prentice Hall,2000. Supplementary reading  Warkely, J.F , “Digital Design: Principles and Practices”2nd ed. Englewood Cliffs, NJ: Prentice Hall, 2000  Mano M M, ”Digital Design” 2nd ed, Englewood Cliffs, N J Prentice Hall ,1991  M.V. Subramanyam , “Switching Theory and Logic Design”, LAXMI Publications(p) LTD,2004  Nelson, V.P, Nagel, H.T., Carroll, B.D. and Irwin,J.D. "Digital Logic Circuit Analysis and Design", NJ: Prentice Hall,1995

94

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

95

MODULE SPECIFICATION

Module title

Data Communication and Protocols

Module code

CS 241

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

BS 154 Mathematical analysis 2 BS 152 Linear algebra 2

Co-requisites

None

Excluded combinations

None

96

LEARNING OUTCOMES Knowledge and understanding        

The analogue and digital signals and data; The concept of bandwidth. binary data; bits and bytes; How a modems work and modulation techniques; The characteristics of the principal transmission media; Factors affecting the capacity of communication channel; The role and functions of the principal components of LANs and WANs; The principles of multiplexing and channel usage; The role of protocols and the characteristics of some widely used ones.

Subject specific skills (including practical/professional skills)      

Specify the practical implementation factors for signal design, data rate & transmission bandwidth of a simple channel; Select, design and implement the proper media for data communication; Use theoretical Theorem (Nyquist's , and the Shannon-Hartley) to calculate the Channel capacity and application to the PSTN and to modem design; Use the error detection & correction methods & their practical aspects; Design and implement a program that allows client-server file transfer; Use the major components of a data communication network: multiplexers, concentrators, repeaters and bridges.

Cognitive skills    

Recognize the history and evolution of modern computer-based communication systems; Carry out network designs using appropriate hardware and software components to provide specified services for a given site; Compare between alternative design approaches for channel usages and multiplexing; Carry out straightforward numerical relating to network and channel capacity for a given application.

Key transferable skills     

Discuss various data communication architectures and protocols; Elaborate on differences of speed, distance, cost and error rates of various transmission media; Quantify the values of channel and signals parameters; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

97

INDICATIVE CONTENT       



Data communication overview; Data transmission; Guided and wireless transmission; Signal encoding techniques; Digital data communication technique; Multiplexing; Circuit switching and packet switching; Asynchronous transfer mode ATM.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  W. Stallings, “Data and Computer Communications”, 7th Edition, Prentice Hall 2004. Supplementary reading  Behrouz Forouzan (2003) Data Communications And Networking 3ed. McGraw Hill , 2004  Andrew S. Tanenbaum, "Computer Networks" 4th Edition, Pearson Education International, Prentice-Hall, 2003

98

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

99

MODULE SPECIFICATION

Module title

Computer Networks 1

Module code

CS 242

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

CS 241 Data communication & Protocols

Co-requisites

None

Excluded combinations

None

100

LEARNING OUTCOMES Knowledge and understanding        

Characteristics and applications of various networking concepts and technologies; Means by which a collection of network protocols co-operate and communicate to achieve the overall networking function; Computer networks’ standards, protocols (OSI and TCP/IP reference models); At least one protocol at each of the main levels of the OSI seven layer reference model; Packet forwarding and the role of routing protocols; Error detection and recovery mechanisms; Means by which features such as flow control and quality of service are achieved; Means by which internetworking demands changes in the operation of basic techniques.

Subject specific skills (including practical/professional skills)   

Specify the implementation of a simple protocol; Design and implement a program that allows client-server file transfer; Use network monitoring tools.

Cognitive skills      

Recognise internetworking concepts, architecture and protocols; Carry out network designs using appropriate hardware and software components to provide specified services for a given site; Compare between alternative computer networks design approaches; Calculate message delays and throughput for a given application; Demonstrate an understanding of C1, A5 and A7, for given scenarios; Analyse network protocols designs.

Key transferable skills     

Discuss various network architectures and protocols; Elaborate on differences of protocols and architectures; Quantify the values of protocol parameters and indicate their advantages and disadvantages; Work co-operatively in a group and share decision making; Develop time-management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

101

INDICATIVE CONTENT      



Introduction The physical layer; The data link layer; The media access control sub-layer; The network layer; The transport layer; The application layer.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Andrew S. Tanenbaum, "Computer Networks" 4th Edition, Pearson Education International, Prentice-Hall, 2003. Supplementary reading  W. Stallings, “Data and Computer Communications”, 7/e, Prentice Hall 2004  J. F. Kurose and D. W. Ross, “Computer Networking (A Top-Down Approach Featuring the Internet)” , 3rd edition, Addison-Wesley, 2005  L. L. Peterson and B. S. Davie, "Computer Networks: a system approach", 2/e, Morgan Kaufmann

102

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 9. A 3-hour unseen examination. 10. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 13. Midterm class test. 14. Assignments and quizzes 15. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 9. A 3-hour unseen examination. 10. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 13. Midterm class test. 14. Assignments and quizzes 15. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

103

MODULE SPECIFICATION

Module title

Logic Programming

Module code

CS 311

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 212 Comp. Programming fundamentals 2

Co-requisites

None

Excluded combinations

None

104

LEARNING OUTCOMES Knowledge and understanding    

Propositional logic; Predicate logic; Logic programming paradigm; Basics of the Prolog programming language.

Subject specific skills (including practical/professional skills)     

Write programs in Prolog using a mixture of recursion, arithmetic, lists/trees, negation, IO and other non-logical features such as assert and retract; Improve the efficiency of a Prolog program using the cut operator; Use Prolog for simple problem solving; Solve search problems, apply effectively transitive closure to a graph, be able to avoid cycles in search problems, be able to apply effectively, depth, bounded and breadth-first searches; Apply Logic programming to hardware simulation.

Cognitive skills  

Read and write logical theories in propositional and predicate logic; Translate logic theories into a form suitable for execution by a computer.

Key transferable skills     

Know how to formulate practical problems in a logical style.; Translate English sentences in logic; Translate logical statements in English; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

105

INDICATIVE CONTENT       



Introduction to Programming and Logic Programming; Unification, data types and built-in predicates; The structured object data type and storing and retrieving facts; Representing information, search strategies and simple rules; Backtracking and recursion; Lists and list processing; Termination criteria; Output and program design.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  L. Sterling and E. Shapiro, The Art of Prolog, 2nd edition.; ISBN: 0262691639, MIT Press, 1994. Supplementary reading  Clocksin WF & Mellish CS, Programming in Prolog, 1994  Clocksin and Mellish: Programming in Prolog, Springer-Verlag, 2003  Marriott and Stuckey: Programming with Constraints, MIT Press, 1999  Bratko, Prolog, Programming for Artificial Intelligence, 3rd edition; ISBN: 0201403757, Addison-Wesley, 2001

106

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

107

MODULE SPECIFICATION

Module title

Algorithms and Data Structures

Module code

CS 312

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

CS 214 Object Oriented Programming 2

Co-requisites

None

Excluded combinations

None

108

LEARNING OUTCOMES Knowledge and understanding            

The theory and practice of implementing algorithms; The mathematical properties of algorithms; Common data structures and algorithms; The implementations of standard abstract data types; The principles underlying all of the standard sorting algorithms and be aware of their complexity; A wide range of searching and sorting algorithms; Several representations of trees, and their applications; Several representations of graphs, and their applications, together with a selection of important algorithms on graphs; Distinguishing between data structures for internal and external storage; The concepts of time complexity; How to code data structures using object oriented methods; Java collection class.

Subject specific skills (including practical/professional skills)       

Have a greater confidence to write programs in Java; Code a simple data structure; Use data structures to build complex algorithms; Implement data structures and algorithms; Evaluate available tools, applications, algorithms and data structures, and select those that are fit for purpose within a given domain/scenario; Analyze and compare solutions to technical problems; Make informed decisions about the most appropriate data structures and algorithms to use when designing software.

Cognitive skills          

Apply mathematical techniques to algorithms and data structures; Understand the specification of data structures and algorithms; Choose the most appropriate data structure for a particular problem; Be familiar with a number of important computer algorithms using those structures; Develop efficient algorithms for simple computational tasks; Evaluate an algorithm for efficiency; Reason the correctness of algorithms; Construct and use the data structures mentioned above; Design and use linked data structures; Analyze the time and space behaviour of simple algorithms.

109

Key transferable skills       

Solve problems using a variety of data structures and algorithms; Make effective use of existing techniques to solve problems; Solve problems algorithmically; Gain further experience in a broad range of IT skills; Learn that the efficiency of a program depends on the choice of data structures used to implement the underlying algorithm; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

INDICATIVE CONTENT     



Introduction to algorithms; Performance of algorithms; Introduction to data structures; Advanced data structures; Searching and sorting algorithms; Tables, Trees, and Graphs.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  M. T. Goodrich & R. Tamassia, "Data Structures & Algorithms in Java", 4th Edition, Wiley, 2006. Supplementary reading  Cormen, Leiserson, Rivest, Stein: “Introduction to Algorithms” 2nd Edition. McGrawHill, 2002  Robert Lafore, “Data Structures and Algorithms in Java”. Sams, 2002  Weiss MA, “Data Structures and Algorithm Analysis in Java” 2nd Edition, AddisonWesley 2006  Collins W Data Structures and the Java Collections Framework 2nd Edition, McGrawHill 2004

110

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

111

MODULE SPECIFICATION

Module title

Computer Architecture

Module code

CS 321

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 221 Digital Hardware

Co-requisites

None

Excluded combinations

None

112

LEARNING OUTCOMES Knowledge and understanding     

Analyse a block diagram of a computer and explain how it works at the level of logic gates; Analyse and develop low level programs and describe how they are executed by a CPU; Describe how a computer performs input and output operations; Explain how abstract concepts in high-level languages, such as function call' or `local variable', are implemented in machine code; Judge the applicability of high and low level language programming.

Subject specific skills (including practical/professional skills)    

Design combinational circuits using gates; Explain and analyse CPU design; Explain and analyse memory design; Explain and analyse I/O device design.

Cognitive skills  

Describe the main components of a computer; Understand differences between the main architectural families.

Key transferable skills     

Think logically and develop the ability to apply logic to a number systems; Solve arithmetical problems involving different computer number systems - binary, octal, hex, 2's complement; Solve Boolean algebra problems involving deMorgan's Laws, truth tables, minterms and maxterms, and Karnaugh maps; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

113

INDICATIVE CONTENT     



Basic concepts; Inside the CPU; Memory architecture; I/O and peripheral control; Hardware/software interface; Networks.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Stallings, Computer Organization and Architecture. Prentice Hall, 2003 (sixth edition). Supplementary reading  David A. Patterson, John L. Hennessy, Computer Organization & Design – The Hardware/Software Interface 2nd ed., Morgan Kaufmann, San Francisco, 1998  John L. Hennessy, David A. Patterson, Computer Architecture – A Quantitative Approach 3rd ed., Morgan Kaufmann, San Francisco, 2003  Andrew S. Tanenbaum, Structured Computer Organization 6th ed., Prenticr Hall, 1999  Miles J. Murdocca, Vincent P. Heuring, Principles of Computer Architecture, Prentice Hall, 1999

114

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

115

MODULE SPECIFICATION

Module title

Software Engineering 1

Module code

CS 331

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

CS 214 Object Oriented Programming 2

Co-requisites

None

Excluded combinations

None

116

LEARNING OUTCOMES Knowledge and understanding      

The principles and practice of the development of software systems; Models of the software development process; Structured analysis and object modelling techniques in the software development process; The use of CASE tools as an aid to object modelling; Software testing, particularly module testing techniques; The professional and legal duties software engineers owe to their employers, employees, customers and the wider public.

Subject specific skills (including practical/professional skills)       

Describe two or more development methodologies; Estimate and plan a small to medium project; Use a CASE tool; Use CASE and UML modelling for a simple system; Select test cases for a software module; Explore advanced features of complex software tools; Install and configure professional software tools.

Cognitive skills    

Debate the suitability for a small to medium project of two or more development methodologies; Choose appropriate tools and techniques to estimate and plan a small to medium project; Analyse the software requirements of a simple system; Plan the testing for a software module.

Key transferable skills    

Demonstrate effective use of general IT facilities; Communicate effectively using appropriate interpersonal skills and using different media; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

117

INDICATIVE CONTENT 

 

   



Introduction and Overview o Waterfall model o Spiral model o Prototyping and incremental development Requirements Analysis Structured Methods o ER-Diagrams o DFD and ELH Diagrams o CASE tools and UML diagrams Object Oriented Methods Architecture and Systems Integration Design to Implementation Validation and Verification o Black box testing o White box testing o Integration Testing strategies Project Planning and Management o Function Point Analysis o COCOMO and other estimation methods

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Sommerville I, Software Engineering (6th Edition), Addison Wesley 2001. Supplementary reading  S. Bennett, J. Skelton, et al. (2001). Schaum's outline of UML, McGraw-Hill  Frost S, The Select Perspective - Developing Enterprise Systems Using Object Technology, Select Software Tools Inc, Santa Ana, CA, 1995  Fowler M & Scott K, UML Distilled, Addison-Wesley 1997

118

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

119

MODULE SPECIFICATION

Module title

Operating Systems

Module code

CS 351

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 312 Algorithms and Data Structures CS 321 Computer Architecture

Co-requisites

None

Excluded combinations

None

120

LEARNING OUTCOMES Knowledge and understanding      

The history of the development of modern operating systems; Designing considerations of an operating system; Process management; Memory management; Device management; Concurrency and synchronization issues.

Subject specific skills (including practical/professional skills)    

Analysing OS characteristics; Selecting algorithms and parameters for desired operating system functionality; Ensuring computer security; Implementing a piece of system-level code in the C programming language.

Cognitive skills  

Evaluate the key issues involved in implementing an operating system; Analyse process management problems and select appropriate software tools and algorithms to deal with them.

Key transferable skills     

Make critical evaluations of competing commercial and "free" products; Construct and recognise the importance of abstraction in software models; Communicate effectively using appropriate interpersonal skills and using different media; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

121

INDICATIVE CONTENT        

Computer system structures; Operating system structures; Process management; Process synchronization: the critical section problem; semaphores; monitors; atomic transactions; Handling deadlocks: prevention, avoidance, detection and recovery; Storage management; I/O systems; Protection and security.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Tanenbaum, A S, Modern Operating Systems 2nd Edition, Prentice Hall (2001). Supplementary reading  Silberschatz,A.M, Galvin, P.B., Gagne,G., Operating Systems Concepts, Wiley&Sons,2002  Tanenbaum,A.S., Van Stern, M.R: Distribute Systems, Prentice Hall, 2002  Stallings W., Operating Systems, Prentice Hall, 2001  Silberschatz,A.M, Galvin, P.B., Gagne,G., Applied Operating Systems Concepts, , Wiley&Sons,2000

122

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

123

MODULE SPECIFICATION

Module title

Internet Technologies

Module code

CS 362

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

CS 242 Computer Networks 1

Co-requisites

None

Excluded combinations

None

124

LEARNING OUTCOMES Knowledge and understanding       

The characteristics and applications of various internet concepts and technologies; The basic concepts and protocols underlying the internet as they pertain to dynamic websites; Demonstrating the ability to successfully utilize internet technology, including e-mail and the WWW, FTP & Audio/Video for communication needs; Constructing static web pages and web sites & Create dynamic and interactive websites; Attaining hands-on skills in using basic internet productivity tools, and gain understanding of related data models and query and mark-up languages; Internet protocols at each of the main levels of the internet layer reference model; Appreciating how internet demands change in the operation of basic techniques.

Subject specific skills (including practical/professional skills)    

Implement HTML, JavaScript, and advanced Internet programming topics; Design and implement a program that allows client-server file transfer; Use the current practice and the future potential in applying internet information systems to achieve organizational goals; Locate best personal opportunities and best priorities for Internet deployment and utilization.

Cognitive skills      

Apply key concepts of Internet Studies; and apply trans-disciplinary thinking to the application and creation of ideas concerning networked technologies of information and communication; Recognize internetworking concepts, architecture and protocols; Assess the usability of a web-site; Compare between alternative internet technologies and design approaches; Optimize multimedia formats for the web; Create database-driven web-sites.

Key transferable skills       

Gain the most up-to-date information on HTML and JavaScript programming skills; Engage in an abundance of interesting and challenging activities and performance; Create, build and manage Internet content and facilitate, develop and manage virtual communities; Use communication skills enabling the IT professional to interact efficiently and effectively with supervisors, peers and end-users; Communicate effectively using appropriate interpersonal skills and using different media; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

125

INDICATIVE CONTENT                

Introduction; Internet: a network of networks; ISPs and network connections; IP addresses and domain names; Electronic mail; Network news: bulletin board service; WWW; WWW documents (HTML); Advanced web technologies; Automated web search (search engines); Audio and video communication; Faxes and files (FTP); Remote login and remote desktop (TELNET); Facilities for secure communications; E-commerce and business The global digital library.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Michael Muckian & Richard Barrett Clements; ”Internet Technology Handbook”, 2002, Aspen  Karl Barksdale, E. Turner; “HTML, JavaScript, and Advanced Internet Technologies”; Course Technology; 1st edition; 2005. Supplementary reading  A Tanenbaum, “Computer Networks”, 4th Edition, Prentice Hall, 2003  F. Halsall, “Computer Networking and the Internet”, 5th Ed, Addison Wesley, 2005  Douglas E Comer “THE INTERNET BOOK”, 3rd ed. Prentice-Hall of India, 2003  Douglas E Comer, “Hands-on Networking with Internet Technologies”, 2/E . Prentice Hall, 2005  Uyless Black, “Advanced Internet Technologies” Prentice Hall PTR; 1st edition (October 8, 1998)

126

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

127

MODULE SPECIFICATION

Module title

Communication Technology

Module code

CS 363

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 241 Data communication & Protocols

Co-requisites

None

Excluded combinations

None

128

LEARNING OUTCOMES Knowledge and understanding     

The basic characteristics of transmitting digital data with digital signals, analogue data with digital signals, digital data with analogue signals, and analogue data with analogue signals; The basic terminology of computer networks; The individual components of the big picture of computer networks; The different multiplexing schemes; The basic principles for designing a simple switching system.

Subject specific skills (including practical/professional skills)  Simulate of a base band PAM binary data transmission system;  Design equalizer in a baseband PAM binary transmission system;  Be familiar with frequency shift keying (FSK), phase shift keying (PSK), quadrature phase shift keying (QPSK);  Be capable of framing and formatting;  Be capable of bandwidth testing and measuring. Cognitive skills        

Understand the basic principles for designing digital communication systems; Understand the different multiplexing schemes; Understand the basic principles for designing a simple switching system; Outline the basic characteristics of transmitting digital data with digital signals, analog data with digital signals, digital data with analog signals, and analog data with analog signals Define the basic terminology of computer networks; Describe the basics of wireless radio, including AMPS, D-AMPS, PCS systems, and third generation wireless systems; Outline the basic multiplexing characteristics of both T1 and ISDN telephone systems; Describe statistical time division multiplexing and list its applications, advantages, and disadvantages.

Key transferable skills       

Expertise in highly specialized and advanced technical aspects of designing a microcontroller for an application; Expertise in advanced application of software using a low/high level language; Transfer and apply diagnostic skills to the application of industry standard techniques to the developed microcontroller system; Exercise appropriate judgment in the areas of planning and design to the application of a microcontroller in the design of a real product; Communicate effectively using appropriate interpersonal skills and using different media; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working. 129

INDICATIVE CONTENT  Transmission Media  Baseband Data Transmission - Analysis of distortion mechanisms and eye diagram, inter-symbol interference (ISI) and pulse shaping. - Baseband binary PAM system, M-ary signalling schemes, equalization.  Digital Carrier Modulation System - ASK, FSK, PSK and QAM modulation. - Error probability performance for various digital modulation schemes.  Digital Multiplexing - Frequency-division multiplexing (FDM) and time-division multiplexing (TDM). - Asynchronous digital multiplexing hierarchy. - Synchronous digital multiplexing, SDH and SONET.  Switching Principles and Systems - Traffic engineering. - Circuit switching packet switching  Data link and MAC sublayer - Data link layer basics: framing, error detection, automatic repeat request - Protocols; common existing protocols: ATM, CSMA, IEEE 802.11. TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  S.S. Haykin, Communication Systems, Wiley, 2001.  W. Stallings, Data and Computer Communications, 6th ed., Prentice-Hall, 2000. Supplementary reading  J.G. Proakis and M. Salehi, Communication Systems Engineering, 2nd ed., PrenticeHall, 2002.  J.E. Flood, Telecommunications Switching, Traffic and Networks, Prentice-Hall, 1994.  E.B. Carne, Telecommunications Primers: Data Voice and Video Communications, 2nd ed., Prentice-Hall, 1999.

130

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

131

MODULE SPECIFICATION

Module title

Computer Security

Module code

CS 412

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 312 Algorithms and Data Structures CS 351 Operating Systems

Co-requisites

None

Excluded combinations

None

132

LEARNING OUTCOMES Knowledge and understanding       

Security issues associated with networked computers; The strengths and weaknesses of available security techniques, including those designed to assist commercial transactions on the internet; The use of those techniques in a protected networked environment; The conditions that produce a need for error detecting and correcting codes; Standard coding techniques and be able to express these as algorithms; The basic principles of encryption/decryption; Standard encryption/decryption schemes and be able to express selected schemes as algorithms.

Subject specific skills (including practical/professional skills)    

Analysis of OS characteristics; Selection of algorithms and parameters for desired operating system functionality; Ensuring computer security; Implementing a piece of system-level code in the C programming language.

Cognitive skills     

Think independently while giving due weight to the arguments of others; Understand complex security issues and relate them to specific situations; Decide whether a particular coding scheme is suitable for a specific application; Compute example outcomes of encoding and decoding using a particular coding scheme; Explain encryption techniques such as RSA and compute simple examples.

Key transferable skills     

Be able to work as part of a team to solve real security problems; Have an enhanced ability to produce detailed reports; Retrieve and synthesize information from independent sources, suitably referenced and formatted; Manage and organize own time; Work independently and as a member of a team.

133

INDICATIVE CONTENT         

Network security fundamentals; Cryptography; Finite fields; Public key and authentication; Authentication applications; Electronic mail security; IP security; Intruders and viruses; Watermarking applications.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Cryptography and Network Security, Principles and Practice (3rd Edition), William Stallings, Prentice Hall, 2003. Supplementary reading  Popa – Computer Security, the notices course, University of Pitesti, Romania, 2004.  Richard Smith, Richard E. Smith - Authentication: From Passwords to Public Keys, Addison-Wesley Pub Co, 2001  John Viega, Gary McGraw - Building Secure Software: How to Avoid Security Problems the Right Way, Addison-Wesley Pub Co, 2003  Mark G. Graff, Kenneth R. Van Wyk - Secure Coding: Principles and Practices, Addison-Wesley Pub Co, 2004.

134

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

135

MODULE SPECIFICATION

Module title

Computer Graphics

Module code

CS 413

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 212 Comp. Programming Fundamentals 2

Co-requisites

None

Excluded combinations

None

136

LEARNING OUTCOMES Knowledge and understanding         

The fundamentals of computer graphics and its applications; The meaning of raster graphics; Simple graphic entities and the rendering environment; The technical issues of 2D lines and line drawing algorithms; The theory and practice of 2D geometry, 2D transformations and homogeneous coordinates; Clipping: clipping lines, clipping algorithms, clipping polygons; Filling: scan conversion, filling polygons and pattern filling algorithms; The basic theory of text and fonts; The fundamentals of colour.

Subject specific skills (including practical/professional skills)   

Write programs using OpenGL and C++; Programme low level algorithms at the pixel level; Programme complex 3-D scenes using a modern computer graphics system, OpenGL.

Cognitive skills    

Design algorithms to raster scan new curves and shapes; Devise the complex 3-D transformations and coordinate system changes required to build complex scenes; Manipulate lighting and material properties to produce pleasing visual effects; Take the basic algorithms and design graphics programming environment.

Key transferable skills    

Appreciate the complexity and effort involved in producing modern computer games, film and television animation; Retrieve and synthesize information from independent sources, suitably referenced and formatted; Manage and organize own time; Work independently and as a member of a team.

137

INDICATIVE CONTENT          

Introduction to computer graphics; Graphical interaction; Mathematical foundations; Transformations; Viewing; Clipping; Hidden Surface Removal; Scan Conversion; Realism; Ray tracing.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Foley J D and Van Dam, Computer Graphics: Principles and Practice (2nd Edition) in C, Addison Wesley 1996 Supplementary reading  Angel E, Interactive Computer Graphics: A top-down approach with Open GL, 4th Edition, Addison Wesley 2006  Hearn D and Baker M P, Computer Graphics with OpenGL 3rd Edition, Prentice Hall 2003  Hill F S, Computer Graphics using OPEN GL 2nd Edition, Prentice Hall 2001  Bourg D Physics for Games Developers O'Reilly 2001  P. Shirley, Fundamentals of Computer Graphics, 1st ed, AK Peters Ltd, 2002.

138

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

139

MODULE SPECIFICATION

Module title

Microsystems

Module code

CS 421

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 321 Computer Architecture

Co-requisites

None

Excluded combinations

None

140

LEARNING OUTCOMES Knowledge and understanding     

The characteristics of RISC and CISC architectures; The typical microprocessor and microcontroller architectures, and the criteria for selecting a microcontroller for a particular application; The development tools for microprocessor based systems; The main concepts, interfaces and peripheral components associated with microprocessor based systems; Appreciation of programming methods in assembler and the C programming language.

Subject specific skills (including practical/professional skills)        

Produce software for a microprocessor-based system; Interface microprocessor-based systems; Be equipped with the skills to tackle the skills aspects of a computer hardware final project; Know how to program interface boards in C code; Have skills in design and developing of software for embedded systems in ‘C’; Implement microcontroller systems for simple control applications intelligent systems; Employ industry standard development and diagnostic software; Demonstrate experience in the application of microcontrollers in the real products.

Cognitive skills     

Investigate microprocessor-based systems and explain the fundamental operating principles of processor based systems; Demonstrate knowledge of interrupt handling, priority pre-emption, and watchdog timers and employ both interrupt driven and polled I/O strategies in software development; Apply programming techniques appropriate to embedded systems; Apply appropriate hardware and software interfaces for basic embedded processor applications; Select an appropriate ‘architecture’ or program design to apply to a particular situation; e.g. an interrupt-driven I/O handler for a responsive real-time machine.

Key transferable skills    

Expertise in highly specialized and advanced technical aspects of designing a microcontroller for an application; Expertise in advanced application of software using a low/high level language; Transfer and apply diagnostic skills to the application of industry standard techniques to the developed microcontroller system; Exercise appropriate judgment in the areas of planning and design to the application of a microcontroller in the design of a real product.

141

INDICATIVE CONTENT          

Microprocessor architectures; Microsystems basic hardware; Interfacing Microsystems; Detailed study of a specified processor system; Structure of an embedded microcontroller system; Programming techniques; Development of embedded microprocessor systems; Microcontroller software development; Debugging and testing techniques; Applications.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  “John Crisp; “Introduction to Microprocessors and Microcontrollers” 2nd Ed.; Newnes 2004  Lawrence A. Duarte "Microcontroller Beginner's Handbook” 2nd Ed.; Prompter 1999 Supplementary reading   

William Kleitz: “Microprocessor and Microcontroller Fundamentals”; Prentice Hall; 1997 W. Valvano; "Embedded Microcomputer Systems: Real Time Interfacing", Jonathan, Brooks Cole, 2000 Arnold S. Berger “Embedded Systems Design: An Introduction to Processes, Tools, and Techniques”, CMP Books, Kansas USA, 2002

142

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

143

MODULE SPECIFICATION

Module title

Distributed Systems

Module code

CS 422

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 321 Computer Architecture

Co-requisites

None

Excluded combinations

None

144

LEARNING OUTCOMES Knowledge and understanding   



The concepts, and design and implementation of distributed systems; The reasons for, possibilities, advantages and disadvantages of deploying distributed technologies in a business context. Fundamental and advanced concepts, principles and techniques from the following topic areas: distributed objects; transactions and their importance to resilience; key project management techniques; software development techniques for a distributed environment; A basic understanding of research methods and techniques appropriate to defining, planning and carrying out a research project within your chosen specialist area within data communications networks and distributed systems

Subject specific skills (including practical/professional skills)     

Design and implement distributed applications using a middleware. Prepare cases advocating the appropriate use of computing systems technologies; Appraise new developments in computing systems technology and assess applicability to a particular workplace scenario or area of academic or professional interest; Demonstrate an understanding of the roles in system development and the responsibilities of those roles. Be able to demonstrate an awareness of the legal and ethical issues associated with implementation of distributed systems and computing in the workplace.

Cognitive skills         

Understand the various architecture models and middleware of distributed systems; Choose a model or a middleware for a particular situation by comparing the attributes of each type in a critical way for a range of typical applications. Analyse new problems, sifting the irrelevant from the relevant and expressing the results using standard formalisms and notations; Integrate knowledge and skills from various sources into a coherent whole, making the appropriate abstractions; Critically evaluate proposed solutions using appropriate proven methods; Evaluate the strengths and weaknesses of a particular technology within a distributed system application; Synthesise arguments from underlying premises to produce overarching conclusions; Deal with complex issues both systematically and creatively, making informed judgments in the absence of complete data; Demonstrate self-direction and originality in tackling and solving problems. Critically evaluate and reflect upon your own work.

Key transferable skills  

Understand the reasons for, possibilities, advantages and disadvantages of deploying distributed technologies in a business context; Have an enhanced ability to produce detailed reports; 145

  

Retrieve and synthesize information from independent sources, suitably referenced and formatted; Manage and organize own time; Work independently and as a member of a team.

INDICATIVE CONTENT           

Introduction to distributed systems; Communication; Processes; Naming; Synchronisation; Consistency and replication; Fault tolerance; Security; Object-based Systems; File systems; Coordination-based systems.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Distributed Systems: Concepts and Design (3rd ed.)Coulouris, Dollimore and Kindberg Addison Wesley, 2000 Supplementary reading  

Distributed Systems (2nd ed.), S Mullender. Addison Wesley, 1999 Distributed Systems for System Architects, P Verissimo and L Rodrigues Kluwer, 2001

146

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

147

MODULE SPECIFICATION

Module title

Artificial Intelligence

Module code

CS 431

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

BS 253 Probabilities & Statistics

Co-requisites

None

Excluded combinations

None

148

LEARNING OUTCOMES Knowledge and understanding          

Different artificial intelligence architectures and understand when and how to use them; How certain problems can be solved by searching and describe the issues involved; The operation of monotonic and non-monotonic reasoning techniques and the distinction between them; Comparing and contrasting the most common models used for structured knowledge representation; The concepts of different planning systems and how they differ from classical search techniques; Comparing and contrasting static world planning systems with those needed for dynamic execution; The advantages and shortcomings of probabilistic reasoning; The basic techniques for representing uncertainty in knowledge; The differences among the three main styles of learning (supervised, reinforcement, and unsupervised) and determine which of them is appropriate for a particular problem domain; The basic concepts of neural networks.

Subject specific skills (including practical/professional skills)   

Programme in Scheme and Prolog; Implement A.I. methods/algorithms; Use A.I. tools relevant for one or more of: data mining, neural networks; genetic algorithms.

Cognitive skills   

Assess the claims of AI practitioners as they relate to `intelligence'; Assess the validity of approaches to model intelligent processing; Assess the applicability of AI techniques in novel domains.

Key transferable skills     

Perform basic risk assessment of working or public environments; Plan technical activities more accurately; Retrieve and synthesize information from independent sources, suitably referenced and formatted; Manage and organize own time; Work independently and as a member of a team.

149

INDICATIVE CONTENT             

Introduction; What is AI; Solving problems by searching; Uninformed search strategies; Comparing uninformed search strategies; Search strategies; Probabilistic reasoning; The semantics of Bayesian networks; Exact inference in Bayesian networks; Extending probability to first-order representations; Other approaches to uncertain reasoning; Fuzzy sets and fuzzy logic; Making simple decisions.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2nd edition, Prentice-Hall, 2003 Supplementary reading  Alison Cawsey (1998) The Essence of Artificial Intelligence Essence of Computing Series. Prentice Hall  E. Rich and K. Knight (1991) Artificial Intelligence 2nd. McGraw Hill  J. Giarratano and G. Riley (1998) Expert Systems: Principles and programming Boston PWS

150

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

151

MODULE SPECIFICATION

Module title

Software Engineering 2

Module code

CS 432

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 331 Software Engineering 1

Co-requisites

None

Excluded combinations

None

152

LEARNING OUTCOMES Knowledge and understanding       

Concepts and terminology of system safety; Risk and hazard analysis in system safety ; The use of formal methods on system development to dependability ; Approaches to systems security assurance; The role and limitations of formal management and quality assurance practices in ensuring software quality; Current and future trends in software management; The significance of metrics in software management.

Subject specific skills (including practical/professional skills)  

Participate constructively in project planning and project management for software development; Perform calculations analyzing risk strategies in software development.

Cognitive skills         

Understand the role and limitations of formal management and quality; Have assurance practices in ensuring software quality; Be aware of current and future trends in software management; Understand the significance of metrics in software management; Evaluate the different models for software project planning; Analyse risk in software planning and building risk-resistant project plans; Apply hazard analysis techniques in critical system development; Write system specifications using the B notation; Analyse and refine specifications using the B toolkit.

Key transferable skills     

Perform basic risk assessment of working or public environments; Plan technical activities more accurately; Retrieve and synthesize information from independent sources, suitably referenced and formatted; Manage and organize own time; Work independently and as a member of a team.

153

INDICATIVE CONTENT 

 

  

Quality Management for Software o Metrics o Reuse o Estimating o Risk Management Quality Management Standards o ISO and TickIt initiative o Accepted process models, (e.g. CMM) Safety-Critical Systems and Fault-Tolerant Systems o Concepts and terminology o Air traffic control case study o N-Version Development o Reliability o Risk assessment in system safety Introduction to hazard analysis techniques: Fault-Tree Analysis, HAZOP, FME Analysis Assurance of system security Overview of formal methods and their role in ensuring safety B formal method: o Notation methodology o Toolkit

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Pressman RS, Software Engineering: A Practitioner's Approach 6th Edition, McGraw Hill, 2004 Supplementary reading  Nancy G. Leveson, Safeware: System Safety and Computers, Addison-Wesley 1995  Neil Storey, Safety Critical Computer Systems, Prentice Hall, 1996  J.-R. Abrial, The B-Book, Cambridge University Press, 1996  K. Lano, The B Language and Method, Springer 1996.  J.B. Wordsworth, Software Engineering with B., Addison-Wesley 1996

154

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

155

MODULE SPECIFICATION

Module title

Digital Signal Processing

Module code

CS 461

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 241 Data Communications & Protocols

Co-requisites

None

Excluded combinations

None

156

LEARNING OUTCOMES Knowledge and understanding     

The significance of digital signal processing in the fields of computing, telecommunications and multi-media technology; The fundamental concepts such as 'linearity' , 'time-invariance', 'impulse response', 'convolution', 'frequency response', 'z-transforms' and the 'discrete time Fourier transform'. as applied to signal processing systems; The application of a design technique for FIR type digital filters known as the "windowing method"; Analogue/digital conversion as required for the digital processing of analogue signals; The discrete Fourier Transform (DFT), its applications and its implementation by FFT techniques.

Subject specific skills (including practical/professional skills)   

Designing and implement digital filters and FFT algorithms and adaptive filters; Applying modern DSP techniques to various practical applications, e.g. speech processing and digital communications; Using the "MATLAB" language and "signal processing toolboxes" for designing, implementing and simulating digital signal processing (DSP) operations as applied to speech, music and multimedia signals.

Cognitive skills   

Analyse and critically demonstrate an in-depth understanding of fundamental and advanced DSP techniques; Apply several design techniques for IIR type digital filters: "pole-zero placement", the "derivative approximation" and the "bilinear transformation" techniques; Specify the "real time" implementation of DSP operations using special purpose fixed point 'DSP microprocessors'.

Key transferable skills    

Solve problems effectively; Collaborate with others in a small group to solve a common problem; Plan technical activities more accurately; Manage and organize time.

157

INDICATIVE CONTENT        

Introduction; Brief review of analogue and digital signal processing systems; Discrete time linear time-invariant (LTI) signal processing systems; Design of FIR digital filters; Introduction to z-transforms and IIR type discrete time filters; Design of IIR type digital filters using analogue filter approximations; Digital processing of analogue signals and other data; Introduction to the discrete Fourier transform (DFT).

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Ifeachor, E.C and Jervis, Digital Signal Processing - A Practical Approach, Pearson, 2001 Supplementary reading  P. Lynn, W. Fuerst,” Introductory Digital Signal Processing”, Wiley, 1994.  E. Ifeachor, B. Jervis, “Digital Signal Processing, A Practical Approach”, Addison Wesley, 1996.  F. Taylor, “Principles of Signals and Systems”, McGraw-Hill, 1994.  N. Jones, J. Mc.K. Watson, “Digital Signal Processing, Principles, Devices and Applications”, IEE Press, 1990.  E. Cunningham, Digital Filtering, An Introduction, Houghton and Mifflin, 1992.

158

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

159

MODULE SPECIFICATION

Module title

Pattern Recognition

Module code

CS 463

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 461 Diiisla oiigla giS lttigi

Co-requisites

None

Excluded combinations

None

160

LEARNING OUTCOMES Knowledge and understanding    

The principle algorithms used in pattern recognition; Information theory and probability theory to provide a theoretical framework for pattern recognition. The theory and the techniques available to extract information from a variety of data; The importance and difficulty of establishing a principled probabilistic model for pattern recognition;

Subject specific skills (including practical/professional skills)   

Apply pattern recognition algorithms in different fields Evaluate the algorithms performance and appreciate the practical issues involved in the study of real datasets; Provide a clear and concise description of testing and benchmarking experiments.

Cognitive skills   

Evaluate classifiers; Select proper methods for solving specific problems; Evaluate Bayes decision theory methods.

Key transferable skills    

Have an enhanced ability to produce detailed reports; Retrieve and synthesize information from independent sources, suitably referenced and formatted; Manage and organize own time; Work independently and as a member of a team.

161

INDICATIVE CONTENT          



Introduction; Bayes decision theory; Parametric approaches; Discriminant functions; Performance assessment; Nonparametric classification; Feature selection; Unsupervised learning; Support vector machines and kernels; Boosting basics; Other types of learning: o Reinforcement o Transduction o semi-supervised o active o transfer learning

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Pattern Classification (2nd. Edition) by R. O. Duda, P. E. Hart and D. Stork, Wiley 2002 Supplementary reading  Pattern Recognition and Machine Learning by C. Bishop, Springer 2006  Statistics and the Evaluation of Evidence for Forensic Scientists by C. Aitken and F. Taroni, Wiley, 2004

162

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

163

MODULE SPECIFICATION

Module title

Project

Module code

CS 492

Level

6

Module leader

University of Wales credit rating

20

ECTS credit rating

10

Module type

Double

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

164

LEARNING OUTCOMES Knowledge and understanding    

Have completed a substantial piece of work, under the direction of a supervisor but demonstrating self-discipline, organization and initiative; Have demonstrated an ability to gain expertise in a particular area of study largely through directed study; Have produced a critical appraisal of their work, evaluating all aspects of their approach; Be practiced in giving industrial quality poster presentations.

Subject specific skills (including practical/professional skills) 

Demonstrate a range of technical skills in the computer-based processing of data and information in the field of the project.

Cognitive skills   

Identify success criteria, evaluate alternative solutions and make design choices; Conduct literature surveys and collect, manage, analyze and evaluate data related to a concrete information processing application; Take a structured approach to the execution of a research or development project in Computing or IT, employing a number of stages in the analysis of the problem and the synthesis of a solution.

Key transferable skills  

Efficiently manage time when working on a project; Communicate project issues, ideas and progress to others with a general grounding in IT, by means of written reports, formal verbal/visual presentations or practical demonstrations.

165

INDICATIVE CONTENT    

Overview of final year project – procedures and deadlines; Evaluating a project; Writing up a successful project report; Presenting a project.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice on the project, providing general instructions and guidance on how to identify a suitable project, write a project proposal, manage a project, write a project report and prepare presentation material. Tutorial sessions will be follow-up and review sessions, giving consultations on specific problems as they arise and on completing project tasks, individually or in small groups. Laboratory sessions will give students guidance on practical issues relevant to the project, allowing for quick feedback on students progress and understanding. Attendance at lectures, tutorials and laboratory will be regarded as compulsory.

INDICATIVE SOURCES Sources will be dependent upon the project topic.

166

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. Written report 2. Presentation and oral defence.

Element weighting 70% 30%

Component B Description of each element 1. Periodical review sessions 2. Gathering information 3. Project implementation

Element weighting 50% 20% 30%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. Written report 2. Presentation and oral defence.

Element weighting 70% 30%

Component B Description of each element 1. Periodical review sessions 2. Gathering information 3. Project implementation

Element weighting 50% 20% 30%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

167

MODULE SPECIFICATION

Module title

Neural Networks

Module code

CS 313

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

BS 152 Linear Algebra 2 BS 154 Mathematical Analysis 2

Co-requisites

None

Excluded combinations

None

168

LEARNING OUTCOMES Knowledge and understanding     

The concepts and techniques of neural networks through the study of the most important neural network models; Sufficient theoretical background to be able to reason about the behaviour of neural networks; How to evaluate whether neural networks are appropriate to a particular application; How to apply neural networks to particular applications, and to know what steps to take to improve performance; Researching literature on neural networks in one particular domain, and be able to put new work into context of that literature.

Subject specific skills (including practical/professional skills)   

MATLAB Neural Networks Toolbox; Resolving the system modelling; Predication and pattern classification problems of basic engineering systems.

Cognitive skills    

Problem formulating and problem solving; Applying abstract models to solve concrete problems of types not previously seen; Lateral thinking; Summarizing and extracting information.

Key transferable skills       

Write solutions in clean and concise manner, Manage time; Produce detailed reports; Learn independently; Problem solving and design skills; Manage and organize own time; Work independently and as a member of a team.

169

INDICATIVE CONTENT 





Introduction o Neural model o neural network topology o Multi-layer perception networks. o Kohonen networks o Hopfield networks o Radial-basis function networks Learning process o Supervised and unsupervised learning; o Hebbian learning o Competitive learning o Boltzmann machine o Back-propagation, Least-Mean-Square algorithm. Applications o Neural network applications to system modelling, control, prediction and pattern classification

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Haykin S, Neural Networks: a Comprehensive Foundation, Macmillan, 1994 Supplementary reading  Schalkoff R J, Artificial Neural Networks, New York, McGraw-Hill, 1997

170

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

171

MODULE SPECIFICATION

Module title

Assembly Language

Module code

CS 314

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 112 Programming Languages CS 221 Digital Hardware

Co-requisites

None

Excluded combinations

None

172

LEARNING OUTCOMES Knowledge and understanding        

The set of fundamental operations a computer can perform; How to build complex computations out of simple operations; "Seeing through" code written in a high-level language; Writing a simple assembly language program and describing the register model of a computer; How numbers are represented in a computer system and operated on by an arithmetic operator; An organization of a pipelined CPU’s data path; Schematic mechanisms that can exploit the advantages of memory caching; How to interface a CPU to hardware devices from a software perspective.

Subject specific skills (including practical/professional skills)      

Use the Text Pad integrated development environment to enter, run, and debug Assembly programs; Assemble, link, debug, and test Assembly programs; Write Assembly programs that use proper style and documentation; Demonstrate the proper use of procedures - linking to an external library, stack operations, defining and using procedures, flowcharts, and top-down structured design; Demonstrate proper use of data transfers, addressing, and arithmetic; Given a problem description, the student should be able to decide on appropriate data transfer and arithmetic instructions, assembly-link-execute cycle, operators, directives, expressions, JMP and LOOP instructions, and indirect addressing.

Cognitive skills   

Design and write simple assembly language programs for the Intel microprocessor; Analyse the function of assembly language programs; Convert numbers into binary, hexadecimal and other standard formats.

Key transferable skills    

Solve problems effectively; Collaborate with others in a small group to solve a common problem; Learn independently; Manage time.

173

INDICATIVE CONTENT  



 

Revision of binary and hexadecimal. ASCII. Basic Microprocessor Organisation. o CPU, ALU and memory. Data, address and control buses. o Fetch, decode, execute. Registers. o Basic instructions - moving data, mathematical and logical operations. Assembly language programming o Mnemonics. Addressing modes. Program counter and branches. o Conditional instructions and flags. Negative number representations. o Use of the carry, overflow and zero flags. Floating point numbers (IEEE 794). o Branch and link - link register. Stacks and stack pointer. Interrupts. Advanced microprocessor architecture o Instruction pipelines. Von Neuman/Harvard architectures. Memory cache. Interfacing o Memory addressing. Memory mapped input and output.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Kip R. Irvine, Assembly Language for Intel-Based Computers 5/e, Prentice Hall, 2006 Supplementary reading  Gibson, "Electronic Processor Systems". Arnold, 2004

174

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

175

MODULE SPECIFICATION

Module title

Knowledge-Based Systems

Module code

CS 332

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

BS 154 Mathematical analysis 2

Co-requisites

None

Excluded combinations

None

176

LEARNING OUTCOMES Knowledge and understanding       

Understand a wide variety of knowledge representation techniques. Understand the role of logic in the modelling of real-world problems. Understand various methods for representing and reasoning under uncertainty. Understand a number of techniques for learning knowledge from data. Understand how expert systems are implemented. Appreciate the distinction between logical consequence and proof methods. Understand the importance of induction and other techniques for reproducing forms of reasoning employed by humans.

Subject specific skills (including practical/professional skills)  

Be capable of using a number of techniques addressed in the lectures to create useful knowledge technologies. Develop an appreciation for Knowledge Based Systems and their architectures

Cognitive skills       

Isolate and organize conceptual elements of simple domains of discourse and identify the structures and processes by which they may be analyzed; Identify the implications of observing or intervening in a network of causal relations. Choose and apply appropriate knowledge representation techniques. Design and implement a small knowledge based system Represent knowledge in a form suitable for an expert system shell. Prove that some clause is a logical consequence of a given program. Apply proof by resolution

Key transferable skills     

Appreciate the specific difficulties of automating routine human thinking processes, including natural language tasks; Acknowledge the benefits and pitfalls of abstraction and. reasoning in a wide range of scenarios; Plan technical activities more accurately; Manage and organize the own time; Work independently and also as a member of a team.

177

INDICATIVE CONTENT 







Knowledge Modelling - Representation and Reasoning: non-probabilistic: ontologies, formalisms, languages, logics - Representation and Reasoning with Uncertainty: bayesian and causal networks - Representation of Natural Language: lexicons, frames, events Knowledge Acquisition - Elicitation : card-sort, repertory grids - Machine Learning : classification; supervised and unsupervised methods - Natural Language Processing: methods, tools, applications : morphology, syntax, semantics Knowledge Retrieval - Queries, NLP, Information Extraction - Information Retrieval, Google Knowledge Actors - Intelligent information agents - Agent architectures - Inter-agent relationships - Agent communication - Agents as mediators - Agents in the Semantic Web - Other applications

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  John F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks Cole Publishing Co., 2000 Supplementary reading  Daniel Jurafsky and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Prentice Hall, 2000  Stuart J Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (2nd Edition) Prentice Hall, 2003 178

   

Ian H. Witten and Eibe Frank Data Mining: Practical Machine Learning Tools and Techniques Second Edition, Morgan Kaufmann, 2005. McGraw KL., Harbison-Briggs K. Knowledge Acquisition: principles and guidelines, Prentice Hall, 1989 Michael Wooldridge. An Introduction to Multi-Agent Systems, Wiley, 2002 Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R, Shadbolt, N., Van de Velde, W., axtd Wielinga, B. Knowledge Engineering and Management The CommonKADS Methodology, The MIT Press, 2000

179

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

180

MODULE SPECIFICATION

Module title

Information and Computer Network Security

Module code

CS 342

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

CS 242 Computer Networks 1

Co-requisites

None

Excluded combinations

None

181

LEARNING OUTCOMES Knowledge and understanding         

Understand the basic principles of security; Understand principles applied in a range of contexts (e.g. OS design, network design, CPU design, network & system administration, eCommerce, etc). Understand the definition of information security, the security system development life cycle, information security policy, standards, and practices, information security professional and employment policies Understand Risk Management and Information Security Maintenance Identify risks, threats, and attacks. Understand Security Systems and Technologies Understand Cryptography and other Information Security related topics, including but not limited to: The significance of cryptography, the role of cryptography in providing confidentiality, authentication, message integrity, and non-repudiation. Discuss major types of attacks and countermeasures. Introduce vulnerabilities associated with the popular applications such as email, web, wireless and instant messaging.

Subject specific skills (including practical/professional skills)           

Compute specific examples of encryption/decryption; Select and implement an encryption/decryption scheme suitable for a specific application; Acquire enhanced systems programming skills; Evaluate and select appropriate security techniques. Understand a variety of generic security threats and vulnerabilities, and identify and analyse particular security problems for a given application. Discuss cryptography and how it formed the foundation for computer and network security. Discuss network media for a secure network. Discuss network topologies that are pertinent to organization's security policy. Discuss intrusion detection systems. Discuss the disaster recovery planning Appreciate the application of security techniques and technologies in solving real-life security problems in practical systems

Cognitive skills     

Formulate a practical solution to a problem, making effective use of time and resources available; Manage personal learning; Exhibit self-discipline and perseverance. Design security protocols and methods to solve specified security problems. Be familiar with current state of art issues and directions of network security.

182

Key transferable skills    

Demonstrate effective use of general IT facilities; Demonstrate management of their own learning and development including time management and organizational skills; Communicate effectively using appropriate interpersonal skills and using different media. Demonstrate attitudes that are beneficial to maintaining the security of a computer/network system and assisting people to use that system or network.

INDICATIVE CONTENT            

Security and privacy – the problems. Illustrations of how problems arise; The “hacker” mind set; The nature of security and the need for creative thinking; Physical and logical security; Machine access; Protection mechanisms; Encryption and encryption building blocks; Current technologies in security, e.g. VPNs, firewalls; Monitoring of traffic & computer usage; Current approaches to breaking security, e.g. Viruses, Trojan Horses, Denial of Service attacks, etc; E-commerce, digital signatures and e-banking; Legal issues, ethics, and personal practice.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  William Stallings, Cryptography and Network Security, 4th Edition, Pearson Education, ISBN: 0131873164  McCarthy, L. (2003) IT Security: Risking the Corporation. USA: Pearson Education. Supplementary reading  Whitman, Mattord, Principles of Information Security, 2nd Edition, Course Technology, 2004, ISBN 0-619-21625-5.  Tanenbaum, A. (2001) Modern Operating Systems. New Jersey: Prentice Hall.  Tanenbaum, A & van Steen, M. (2002) Distributed Systems: Principles & Paradigms. New Jersey: Prentice Hall  Northcutt, S et al. (2003) Inside Network Perimeter Security. Boston: New Riders (Pearson Education) 183

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

184

MODULE SPECIFICATION

Module title

Computer Networks 2

Module code

CS 341

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 242 Computer Networks 1

Co-requisites

None

Excluded combinations

None

185

LEARNING OUTCOMES Knowledge and understanding        

Computer networking issues and their relationship to programming languages; Program development at different levels of the OSI model; The “big picture” in relating networking issues to other themes in computer science; Networking protocols at each layer of the OSI model; The differences between protocols and APIs (application programming interfaces); How the OSI model compares with vendor specific models. Techniques to anticipate and prepare for a variety of unknown situations that might impact the operation of a computer system or network. How computers communicate with each other and the methods employed to assure that the communication is reliable.

Subject specific skills (including practical/professional skills)    

Use of network simulators to understand how Ethernet and ATM networking protocols operate; Appreciate of issues for supporting real time & multimedia traffic over public networks. Participate in a structured internship based in the workplace and receive real world, hands on experience. Demonstrate knowledge of network management activities and procedures.

Cognitive skills       

Use of network simulators to understand how Ethernet and ATM networking protocols operate; Appreciate of issues for supporting real time and multimedia traffic over public networks. Demonstrate knowledge of network applications. Perform network installation procedures. Perform network maintenance and diagnostics and testing. Demonstrate knowledge of concepts and techniques used in working with communications systems. Demonstrate knowledge of telecommunications networks.

Key transferable skills      

Solve problems effectively; Collaborate with others in a small group to solve a common problem; Plan technical activities more accurately; Manage and organize time. Demonstrate oral and written communication skills and increase ability to be effective team members. Demonstrate confidence to work independently to setup and maintain computer and networking systems.

186

INDICATIVE CONTENT  





Introduction to Networks Local Area Networks verses Wide area computing and management protocols - Components of a LAN: routers, bridges, hubs, switches, repeaters - Major LAN standards and use within components - Example network: Fibre Distributed Data Interface (FDDI) - Wide area networks and differences: - Asynchronous transfer mode (ATM), ISDN, ADSL,X.25 and Frame Relay Internetworking standards - The TCP/IP, DNS - Firewalls and Gate Keepers -- tunnelling protocols - Quality of Service and Service guarantees - (Simple Network Management Protocol (SNMP), HTTP, FTP, Telnet, RSVP, RTP) - TCP congestion control - Application support and protocols (POP3, IMAP4 etc) - Internet services: dial-in lines, PPP, SLIP, CSLIP - Virtual Private Networks (VPNs) - Mobile IP and Wireless Networks Emerging Themes - Mobile and portable computing - Network management and dynamic networking - Networking and embedded systems

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  “Computer Networking: A Top-Down Approach Featuring the Internet'”, James Kurose and Keith Ross, Addison Wesley; 3rd Edition. Supplementary reading  Andrew Tanenbaum, Computer Networks, 4th Edition, Prentice Hall PTR, 2002  James Kurose, Keith Ross, Computer Networking A Top-Down Approach Featuring the Internet, 3rd Edition, Addison-Wesley Professional, USA, 2004  Priscilla Oppenheimer, Top-Down Network Design, Cisco Press, 2004  W. Richard Stevens, The Protocols (TCP/IP Illustrated, Volume 1), Addison-Wesley Professional, 1995 187

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

188

MODULE SPECIFICATION

Module title

Multi-Agent Systems

Module code

CS 414

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 431 Artificial Intelligence

Co-requisites

None

Excluded combinations

None

189

LEARNING OUTCOMES Knowledge and understanding        

What is the Agent, artificial agent; Applications of agent; Knowing about in-depth agent-based systems and technologies used to build them; In-depth the theory of multi-agent systems; Knowing about in-depth a selection of agent application areas; A basic knowledge of most of the course material and can analyze and design familiar systems; The course material and can analyze and design most unfamiliar systems; The course material and an ability to analyze and design to a high standard.

Subject specific skills (including practical/professional skills)    

Implement a non-trivial agent-based system. Construct of intelligent agents Develop a meaningful agent-based system using a contemporary agent development platform Design of a society of agents cooperating to solve specific problems as well as open societies of heterogeneous autonomous agents

Cognitive skills    

Familiarity with the latest agent technologies; Familiarity with current agents trends and future directions; Knowledge of the issues involved in the design of a software agent.; Ability to select or advice on the most appropriate technology for an agent-based system.

Key transferable skills      

Practices of well-engineered design; Decision-making skills; Writing skills; Presentation skills; Awareness of financial, legal, ethical issues; Awareness of issues for disadvantaged people.

190

INDICATIVE CONTENT   

  

  

Intelligent Agents: Agent programming languages. Agent communications. - Agent interaction protocols. - Distributed problem solving and planning. - Search algorithms for agents. - Distributed rational decision making. - Learning in multi-agent systems. Application areas for multi-agent systems Review of multi-agent systems. Agent application areas: - Intelligent Agents - Knowledgeable Agents - Web-based Agents - Conversational Agents - Affective Agents - Agents in Virtual Environments Agents for Visualization Agents for Games Robotics

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  M.J. Wooldridge, An introduction to multi-agent systems, Wiley 2002 Supplementary reading  Architectural Design of Multi-Agent Systems: Technologies and Techniques, Hong Lin, 2007  Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence ,Jacques Ferber 1999  Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology), Fabio Luigi Bellifemine,2007 191

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

192

MODULE SPECIFICATION

Module title

Natural Language Processing

Module code

CS 415

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 431 Artificial Intelligence

Co-requisites

None

Excluded combinations

None

193

LEARNING OUTCOMES Knowledge and understanding  

An overview of Natural Language Processing and its applications, followed by introductions to morphology, syntax and semantics; Linguistic theory and appropriate algorithms for their computational implementation.

Subject specific skills (including practical/professional skills)    

Compete in problem identification and analysis, and design and implementation of software; Use techniques relevant to artificial intelligence and language processing; Compete in handling a variety of symbolic notations, form computer programming languages through diagrammatic formalisms to logics; Be aware of available tools and software to aid in construction of linguistic and language processing software.

Cognitive skills       

Frame hypotheses; Analyse and interpret diverse data; Test theory with observation; Apply numerical and reasoning skills; Demonstrate research design; Critically review scientific literature; Think flexibly and laterally.

Key transferable skills     

Communicate effectively by oral, written and graphical means; Make full use of information technology: e-mail, word-processing, citation indexes, the web; Retrieve and synthesize information from independent sources, suitably referenced and formatted; Manage and organize own time; Work independently and as a member of a team.

194

INDICATIVE CONTENT 

 



 

Introduction - What is language and Natural Language Processing? o What does it mean to know a language? o What do we know when we know a natural language? o What is meant by Natural Language Understanding Word-level descriptions o Morphology and morphological processing o The lexicon Syntax and Syntactic Processors o Lexical and syntactic categories o Introduction to the terminology of syntax and context-free grammars o Part-of-Speech Tagging o From Finite State Automata to Context-Free Grammars o Definite Clause Grammar and the Very Simple Parser o Active Chart Parsers o Unification-based grammars and parsing Semantics and pragmatics o Introduction to semantics and semantic processing o Building semantic structures o Building more complex structures and semantic interpretation o Discourse structure and reasoning Application of Natural Language Processing Machine translation

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Speech and language processing: an introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Jurafsky D & Martin J H. Prentice Hall, 2000 Supplementary reading  Natural language understanding (2nd ed.).Allen J.Benjamin/Cummings, 1995

195

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

196

MODULE SPECIFICATION

Module title

Image Processing

Module code

CS 462

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science

Pre-requisites

CS 461 Digital Signal Processing

Co-requisites

None

Excluded combinations

None

197

LEARNING OUTCOMES Knowledge and understanding   

Understand how computers can process digital images; Know some of the basic operations (their basis, implementation and consequences) in image processing and computer vision.; Know of the relation to signal processing and other fields.

Subject specific skills (including practical/professional skills) 

   

Be familiar with this new area of spatial signal processing; Be able to deploy and benefit from new techniques. Critically understand an extensive range of image processing problems & potential solutions. Acquire practical knowledge of limitations of techniques to accompany detailed theoretical knowledge. Use of specialist image processing tools in the implementation of techniques.

Cognitive skills     

Able to critically review, evaluate and implement a range of techniques in image processing. Able to communicate there findings through demonstrations and presentations. Able to communicate effectively in a group based project and take responsibility for the outcome of individual work and work of the group. Understand the processes of two-dimensional data together with any perceived similarity with the human vision system. Apply image processing & analysis techniques in a range of application scenarios

Key transferable skills     

Skills in exploring relationships between human and computer vision; Temporal and spatial signal processing; Interpretation of spatial data; Time management; Working independently and as a member of a team.

198

INDICATIVE CONTENT         

Visual perception; Image hardware and software; Image formation; Image transforms; Image enhancement; Image restoration; Implementations; Image interpretation; Automatic gait recognition.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Nixon M S and Aguado A S, Feature Extraction and Image Processing Butterworth Heinmann (Newnes), 2002. Book Supplementary reading  Sonka M, Hlavac V and Boyle R, Image Processing, Analysis and Machine Vision, (Chapman and Hall, 2nd Ed. 1999)  Gonzalez et al., Digital Image Processing, Prentice Hall, 2001  Stockman and Shapiro, Computer Vision, Prentice Hall, 2001  Jain A K, Fundamentals of Digital Image Processing, (Prentice Hall, 1989)  Efford, N., Digital Image Processing Using Java,, Addison Wesley, 2000

199

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

200

MODULE SPECIFICATION

Module title

Multimedia

Module code

CS 471

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Science

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc. (Hons) Computer Information Systems

Pre-requisites

CS 214 Object Oriented Programming 2

Co-requisites

None

Excluded combinations

None

201

LEARNING OUTCOMES Knowledge and understanding     

The current key issues in multimedia research; Specific and detailed knowledge and understanding in a number of chosen topic areas of multimedia research. The different types of multimedia devices and common systems and networks in which they are used Possess an awareness of the underlying infrastructure of multimedia systems with relevance to the hardware and software components required. Possess a technical appreciation of core multimedia technologies and standards for Digital Audio, Graphics, Images and Video.

Subject specific skills (including practical/professional skills)     

Develop and integrate a broad range of skills necessary to research and communicate technical concepts and understandings. Demonstrate an awareness of the skills and components needed to author a multimedia presentation. Demonstrate understanding of the fundamental concepts behind multimedia devices Identify the relevant software and hardware components which integrate with multimedia devices to form a complete multimedia system Develop and integrating a broad range of skills necessary to research and communicate

Cognitive skills   

Analyse, review and critically evaluate the work of peers; Analyse, review and critically evaluate your own work; Explain the inter-relationship of areas of computer science research and the ways in which the convergence of technologies drives the development of Multimedia Systems.

Key transferable skills    

Communicate technical information in a written format, and to an agreed academic convention; Communicate technical information in a verbal format; Work collaboratively with others to achieve greater understanding of a technical issue; Study independently and make personal choice in study.

202

INDICATIVE CONTENT 









Fundamental Principles - Definitions - Formats and standards - Compression techniques - Storage - Networking Issues Hypermedia systems - History - The World-wide Web - Open Hypermedia Systems - Content Based Navigation The "Audio Case Study" - Audio and the Web - Digital audio broadcast - Content based navigation in audio Multimedia Information Systems - Multimedia information retrieval - Distributed multimedia systems - The role of software agents Applications - Interactive television - Video-conferencing - Video-on-demand - Educational applications and authoring - Industrial applications - Multimedia archives and digital libraries - CSCW

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

203

INDICATIVE SOURCES Core text 

Sloane A, Multimedia Communication, McGraw-Hill, 1996.

Supplementary reading    

Lowe D and Hall W, Hypermedia and the Web - An Engineering Approach, Wiley 1999. ISBN 0-471-98312-8 Library - Computer Science Collection http://www.library.soton.ac.uk/subjects/ecs/index.shtml IAM Research Grouphttp://www.iam.ecs.soton.ac.uk Fundamentals of Multimedia Ze-Nian Li Mark S Drew Prentice Hall 2004 http://www.cs.sfu.ca/mmbook/ISBN: 0-13-061872-1

204

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

205

Section 4 Modules in the Information Systems Field Compulsory Modules in the Information Systems Field Module Code IS 221 IS 312 IS 321 IS 322 IS 341 IS 342 IS 351 IS 413 IS 418 IS 421 IS 432 IS 441 IS 442 IS 443 IS 492

Module Name Database Theory and Design Analysis and Design of Information Systems 1 Database Management Systems 1 Database Management Systems 2 Modelling and Simulation Introduction to Operations Research and Decision Support Systems Internet Applications Management Information Systems Analysis and Design of Information Systems 2 Object Oriented Database Data Storage and Retrieval Decision Support Systems and Applications Linear and Integer Programming Decision Support Tools and Techniques Project

206

Level 4

Credit Rating 10

5

10

5 5 5

10 10 10

5

10

5 6

10 10

6

10

6 6

10 10

6

10

6

10

6

10

6

20

Optional Modules in the Information Systems Field Module Code IS 311 IS 331 IS 345 IS 352 IS 411 IS 412 IS 416 IS 431 IS 445

Module Name Internet Information Systems Data Warehousing Advanced Modelling and Simulation E-commerce Information Systems Development Methodologies Intelligent Information Systems Geographical Information Systems Data Mining Computational Intelligence in Operations Research and Decision Support

207

Level 5 5 5 5

Credit Rating 10 10 10 10

6

10

6 6 6

10 10 10

6

10

MODULE SPECIFICATION

Module title

Analysis and Design of Information Systems 1

Module code

IS 313

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

CS 331 Software Engineering 1

Co-requisites

None

Excluded combinations

None

208

LEARNING OUTCOMES Knowledge and understanding        

The approach and obligations of the professional systems analyst and the analogies between software and other branches of engineering; The aims and objectives of an information system in the context of a human activity system; Appreciation of the software life cycle, and the management issues involved in software Development; The need for quality assurance and know how it is applied in the software industry; A variety of analysis and design techniques to document existing information systems, to propose alternative new systems, and to specify required information systems; Analysis, design techniques and methods to meet the special needs of distributed information systems and object oriented information systems; The production of the key deliverable of the software life cycle; Appreciation of the use of Project Management tools in software projects.

Subject specific skills (including practical/professional skills)      

Analyze and describe some aspect of the real world in terms of a conceptual model and to employ CASE tools in the design of software systems. Describe and model the concept of a system’s identification and its boundaries with the outside world Describe and apply various structured analysis and design techniques Critically evaluate the modelling process by establishing consistency between the output of the different modelling techniques Apply available automated CASE tools for diagrammatic modelling and documentation consistency Explore creative solutions to problems without precise solution and/or with multiple alternative solutions

Cognitive skills     

Analyze and design a complete software system for a specified application domain. Estimate trade-offs, make decisions and critically evaluate their consequences at different stages of software development process Organize and present technical material in a professional manner Contribute to the work of a group working on analysis and design tasks Carry out peer evaluation and personal reflection

Key transferable skills   

Apply a rigorous approach to software design methodology; Work collaboratively with others to achieve greater understanding of a technical issue; Study independently and make personal choice in study.

209

INDICATIVE CONTENT                

The Systems Development Environment The Origins of Software Managing the Information Systems Project Identifying and Selecting Systems Development Projects Initiating and Planning Systems Development Projects Determining System Requirements Structuring System Process Requirements Structuring System Logic Requirements Structuring System Data Requirements Designing Databases Designing Forms and Reports Designing Interfaces and Dialogues Finalizing Design Specifications Designing Distributed and Internet Systems System Implementation Maintaining Information Systems

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Jeffrey A. Hoffer, Joey F. George, and Joseph S. Valacich, "Modern Systems Analysis and Design," 4th Edition, Prentice Hall, May 17, 2004 Supplementary reading  E. Avison and G. Fitzgerald, "Information Systems Development: Methodologies, Techniques, and Tools " 2nd edition, McGraw-Hill Companies, February 4, 1998  Arthur M. Langer," Analysis and Design of Information Systems," 3rd edition, June 2007  Alan Dennis, Barbara Haley Wixom," Systems Analysis And Design," 3rd edition, Wiley, October 14, 2005

210

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

211

MODULE SPECIFICATION

Module title

Database Theory and Design

Module code

IS 221

Level

4

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

CS 112

Co-requisites

None

Excluded combinations

None

212

Programming Languages

LEARNING OUTCOMES Knowledge and understanding      

Concepts and fundamentals of Database modelling and design; Database languages and facilities provided by database management systems. The different techniques for implementing database systems. The representational power of the three traditional types of DBMS - hierarchical, network, relational; Database languages and facilities provided by database management systems. Understanding how to reverse engineer a database schema

Subject specific skills (including practical/professional skills)       

Design database system Administer database systems Analyse a conceptual schema and transform it into the schema of an appropriate database management system; Compare alternative DBMS schemas for a conceptual model assessing their strengths and weaknesses Handle version and configuration management in modern database systems. Link databases to Web interfaces; Interoperate in heterogeneous distributed databases.

Cognitive skills    

Apply database theory to solve practical problem. Demonstrate skill in analysis and manipulation of the material written within the course. Use for logical argument. Work with relatively little guidance.

Key transferable skills     

Follow Current trends in Database system. Deal with Commercial Database systems. Deal with various computer based information systems. Work co-operatively in a group and share decision making. Develop time management and organizational skills as evidence by the ability to plan and implement efficient effective and effective modes of working.

213

INDICATIVE CONTENT       

Basic Concepts Database Models and Languages Database Design Database Security and protection Current Trends in Database Systems Data warehouse Overview of data mining technology

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  C.J Date, "An Introduction to Database Systems", Pearson Education Inc, 2004. Supplementary reading  Elmasri, R., Navathe, SB., "Fundamentals of database systems", 5th Ed, Addison Wesley, 2006.  Garcia-Molina, H., Ullman, J., Widom, J, " Database Systems: the complete book, Prentice Hall, 2002.  Silberschatz, A., Korth, H.F., Sudarshan, S., " Database System Concepts, 5th Ed, Mc Graw Hill book Co., 2005

214

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

215

MODULE SPECIFICATION

Module title

Database Management Systems 1

Module code

IS 321

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

IS 221 Database Theory & Design

Co-requisites

None

Excluded combinations

None

216

LEARNING OUTCOMES Knowledge and understanding          

Setting up and interrogating a single-table database Producing well-designed forms, menus and reports from it Explaining the reasons for using a multi-table database Recognizing the problems leading to modification anomalies and be able to address simple examples of them using the relational model and normalization Designing simple multi-table databases Construction and use of a simple multi-table database The characteristics, strengths and limitations of current database systems. Relational data models, relational algebra, and structured query languages. Entity Relationship modelling and design. ERM to RM conversions.

Subject specific skills (including practical/professional skills)       

Demonstrating a range of technical skills in the computer-based processing of data and information Developing, implementing and using of a database. Developing data models to represent various data environments Creating a logical data model that identifies entities, attributes and relationships Normalizing data to create stable table structures Building a database to correspond to a logical database design Constructing simple SQL queries to access the database

Cognitive skills   

Identify success criteria, evaluate alternative solutions and make design choices. Conduct literature surveys and collect, manage, analyze and evaluate data related to a concrete information processing application. Take a structured approach to the execution of a research or development project in Computing or IT, employing a number of stages in the analysis of the problem and the synthesis of a solution.

Key transferable skills    

Follow current trends in database management systems. Deal with the various computer-based information systems. Efficiently manage time when working on a project. Communicate project issues, ideas and progress to others with a general grounding in IT, by means of written reports, formal verbal/visual presentations or practical demonstrations

217

INDICATIVE CONTENT        

Introduction Tables and forms Interrogating a Database Reports Multi-table databases Normalization Other Database Management System Facilities Debriefing

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Ramez Elmasri, Shamkant B. Navathe, Fundamentals of Database Systems, 5th ed, Addison Wesley, march 2006 Supplementary reading  Hector Garcia-Molina , Jeffrey D. Ullman, Jennifer D. Widom, "Database Systems: The Complete Book US edition", Prentice Hall, October 2, 2001  Michael Stonebraker and Heller, "Readings in Database Systems (Mogan Kaufmann Series in Data Management Systems) 3rd edition", Morgan Kaufmann Publishers, July 15, 1998  Sliberschatz, A., Korth, H.F., Sudarshan, S., "Database System Concepts 4th edition", McGraw-Hill College, August 1, 2005  Abraham Silberschatz, Henry F. Korth, S. Sudarshan, "Database Systems Concepts 5th edition", McGraw-Hill, May 17, 2005

218

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

219

MODULE SPECIFICATION

Module title

Database Management Systems 2

Module code

IS 322

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 321 Database Management Systems 1

Co-requisites

None

Excluded combinations

None

220

LEARNING OUTCOMES Knowledge and understanding         

Demonstrating knowledge of key approaches for indexing, query processing and optimization in databases Understanding of the underlying elements, assumptions and implications of these approaches in database design and performance Understanding the principle of transactions in database applications Understanding how concurrency in transaction execution works Understanding the various failures that may occur during transaction execution. Knowing the techniques to recover from system failures. Understanding the limitations of the relational model Understanding the principles of object-relational DBMSs Having an appreciation of what the advantages of other non-relational DBMSs (e.g. deductive, object-oriented, nested relational).

Subject specific skills (including practical/professional skills)    

Design a physical database Design and create structured indexes. Design and create queries Design and deal with the different features of the Non-relational database models

Cognitive skills   

Ability to evaluate and criticize methods for indexing and optimization Ability to evaluate the effectiveness and limitations of the relational model Ability to evaluate the effectiveness of different query compilation strategies with respect to query execution time.

Key transferable skills    

Follow current trends in database management systems. Deal with the various computer-based information systems. Efficiently manage time when working on a project. Communicate project issues, ideas and progress to others with a general grounding in IT, by means of written reports, formal verbal/visual presentations or practical demonstrations

221

INDICATIVE CONTENT          

Database system concepts and architecture The entity-relationship (ER) model and the enhanced entity-relationship (EER) model The relational data model: Concepts, Constraints, Languages, Design, and Programming Database Design Theory and Methodology Data Storage, Indexing, Query Processing, and Physical Design Transaction Processing Concepts Object and Object-Relational Database Database Security Advanced Data Modeling and Distribution Emerging Technologies

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Ramez Elmasri, Shamkant B. Navathe, Fundamentals of Database Systems, 5th ed, Addison Wesley, march 2006 Supplementary reading  Hector Garcia-Molina , Jeffrey D. Ullman, Jennifer D. Widom, "Database Systems: The Complete Book US edition", Prentice Hall, October 2, 2001  Michael Stonebraker and Heller, "Readings in Database Systems (Mogan Kaufmann Series in Data Management Systems) 3rd edition", Morgan Kaufmann Publishers, July 15, 1998  Sliberschatz, A., Korth, H.F., Sudarshan, S., "Database System Concepts 4th edition", McGraw-Hill College, August 1, 2005  Abraham Silberschatz, Henry F. Korth, S. Sudarshan, "Database Systems Concepts 5th edition", McGraw-Hill, May 17, 2005

222

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

223

MODULE SPECIFICATION

Module title

Modelling and Simulation

Module code

IS 341

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

CS 214 Object Oriented Programming 2

Co-requisites

None

Excluded combinations

None

224

LEARNING OUTCOMES Knowledge and understanding      

The key ideas of performance modelling and the trade-offs between timeliness and efficient use of resources. How to demonstrate this by an ability to give an account of these ideas and explain why the trade-off occurs. The operational laws and be able to apply them to any system which satisfies the appropriate conditions to derive further information about the system. Furthermore they will be able to assess from a system description whether the conditions are met. How to design, construct and solve a simple performance model based on a Markov process in various high-level modelling formalisms as well as directly at the state transition level. How to give an account of the underlying mathematics and the concept of steady state. How to give an account of, the assumptions which must be made about a system in order to model it as a Markov process.

Subject specific skills (including practical/professional skills)    

Designing, constructing and solving a simple performance model based on simulation, and instrument that model in order to derive performance measures. Developing the skills to analyze a system description and abstract from it to create a model with an appropriate level of detail. Developing judgment with respect to choosing an appropriate modelling technique for a given scenario, so that when given a description of a problem, and the resources and skills available, Recommending the best-suited modelling formalism and solution technique.

Cognitive skills   

Have Effective quantitative problem solving and decision making skills. Create, evaluate and access a range of options, together with the capacity to apply ideas and knowledge to a range of business and other situations. Analyse and interpret presented data.

Key transferable skills     

Problem formulation and decision making Maintain communication skills Develop IT skills in context Efficiently manage time when working on a project. Communicate project issues, ideas and progress to others with a general grounding in IT, by means of written reports, formal verbal/visual presentations or practical demonstrations

225

INDICATIVE CONTENT             

Introduction. Architectures of simulation systems and their classification. Principles of simulation system design and implementation. Modelling and Simulation-Based Development of Systems. Multi-models, multi-paradigm modelling and simulation, multi-resolution modelling and simulation. Architectures of simulators. Examples of multi-paradigm modelling: Processes, FSA, Petri nets, DEVS. Object-oriented and component approaches to modelling and simulation. Parallel and distributed simulation. Anticipatory systems. Nested simulation. Reflective simulation. Architectures for multi-simulations. Cloning, independent time axes. Optimization, adaptation, learning. Modelling and simulation of intelligent systems. Soft computing and simulation. Architectures for multi-agent simulations. Complex systems simulation. Visualization. Interactive simulation

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Fishwick P.: Simulation Model Design and Execution, Prentice Hall, 1995 Supplementary reading  Ross S.: Simulation, Academic Press, 2002  Zeigler B., Kim T., Praehofer H.: Theory of Modelling and Simulation. Academic Press Inc.,U.S.; 2nd Edition. 2000.  Sarjoughian H., Cellier F.: Discrete Event Modelling and Simulation Technologies: A Tapestry of Systems and AI-Based Theories and Methodologies. Springer-Verlag New York Inc. 2001.  Law A., Kelton D.: Simulation Modelling and Analysis, McGraw-Hill, 1991

226

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

227

MODULE SPECIFICATION

Module title

Introduction to Operations Research and Decision Support Systems

Module code

IS 342

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

228

LEARNING OUTCOMES Knowledge and understanding                   

Operational Research techniques in project management, queuing analysis, simulation, stock control and decision analysis. Decision Making The relation of Decision Making to planning The Occasions for Decisions Routine and non routine Decisions Difference between Objective and Bounded Rationality The steps of modelling Payoff matrix Decision Making under Certainty and under Risk Linear Programming and its limitations. Modelling a Linear Programming Problem for any variables The constraints in Linear Programming Why Information systems are required in decision making Decision Support Systems Transaction Processing systems Management Information systems Expert Systems The Traditional Approach and Data (User) Oriented Approach in Databases Effect of Management Level on Decisions

Subject specific skills (including practical/professional skills)          

Solve a Linear Programming Question analytically and graphically Prepare a Linear Programming Question Solve Decision Making Under Risk Problems via Expected Values Calculation Solve Decision Making Under Risk Problems via Decision Trees Formulate a problem for Decision Making Under Risk Understand Decision Making Under Uncertainty Solve Decision Making Under Uncertainty via Maximum Method Solve Decision Making Under Uncertainty via Minimum Method Solve Decision Making Under Uncertainty via Hurwitz Method Solve Decision Making Under Uncertainty via Equally Likely Methods

Cognitive skills  

Analyze and solve some problems with popular Operational Research methods and techniques. Differentiate between different categories of Decision Making

Key transferable skills     

Build models for simple problems in managerial decision making. Utilize proper mathematical methods to solve models. Structure practical problems. Develop and run computer simulation models. Demonstrate writing skills. 229

INDICATIVE CONTENT                   

Environmental policy design and the economics of crime On cross decomposition for mixed-integer programming Optimal and neuro-dynamic programming solutions for a stochastic inventory transportation problem Modelling approaches for median relations A note on the “More-for-Less” paradox. Project experience and some principal considerations concerning operations research. Concepts, aims and problems which modelling in OR and OOP have in common. On the use and misuse of holding cost models. From the publisher to the wholesalers- Determination of the number of copies and their distribution among the wholesalers Local search for a travelling salesman problem in the production control Transportation problems Operational planning and optimization of Hub-and spoke transportation networks for area wide groupage services Stochastic programming: achievements and open problems. Decision making based on causal graphs On the effect of the bargaining context on bargaining behaviour Income tax rate: properties and implications Knowledge management-challenges and the “KNOWING”- implementation strategy Perturbation heuristics for unconstrained quadratic 0-1 programming and an alternate stopping and comparison criterium Solving conflicts and sanctions: results of an experimental study

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  Peter Kischka, Ulrike Leopold-Wildburger, Rolf H. Möhring, and Franz-Josef Radermacher, "Models, Methods and Decision Support for Management: Essays in Honor of Paul Stähly," 1st edition, Physica-Verlag Heidelberg, Mar 1, 2001 Supplementary reading  Edmund K. Burke and Graham Kendall, "Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques," Robert Klein, Oct 31, 2003  Robert Klein, "Scheduling of Resource-Constrained Projects (Operations Research/Computer Science Interfaces Volume 10)," Springer, Nov 30, 1999  Clyde W. Holsapple, Andrew B. Whinston, and C. W. Holsapple, "Recent Developments in Decision Support Systems (Research Reports Esprit)," Springer, April 1993  Robert H. Bonczek, "Foundations of Decision Support Systems (Operations research and industrial engineering)," Academic Press Inc, Jul 1981 230

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

231

MODULE SPECIFICATION

Module title

Internet Applications

Module code

IS 351

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Science BSc.(Hons) Computer Information Systems

Pre-requisites

IS 362 Internet Technologies

Co-requisites

None

Excluded combinations

None

232

LEARNING OUTCOMES Knowledge and understanding    

The basic principles of web page design and be able to write a basic web page using a web authoring tool. The basic principles of web site design and be able to construct a small site of interconnected pages with first and second level navigation The basic concepts of data structuring have acquired the skills to be able to implement a simple database application involving a simple user interface Some common component object

Subject specific skills (including practical/professional skills)     

Make effective use of a range of tools, such as a web browser and email client. Build simple web pages by writing HTML. Write simple Script code, embedded in HTML, to interact with the browser, generate content, and check input. Link a variety of client applications to web pages Incorporate VBA code in components to improve usability, robustness and reliability of a client application

Cognitive skills       

Demonstrate comprehension of the trade-offs involved in design-choices. Recognize and be guided by social, professional and ethical issues and guidelines. Explain the distinction between structure, content and presentation of web material and the benefits of maintaining that distinction. Describe the client-side environment and its potential in providing web features. Describe the server-side environment and its potential in providing web features. Explain the role and potential of databases in providing web features. Describe the main features of XML in the context of the web.

Key transferable skills      

Make effective use of IT facilities for solving problems. Manage their own learning and development, through self-directed study and working on continuous assessments. Develop problem–solving skills; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

233

INDICATIVE CONTENT             

Introduction Outline of the architecture of the web and the associated technologies Content, structure and presentation Interaction with browsers Designing Display and Navigation Toward a Better Design Server-side code Content generation. Code embedded in HTML Server-side Gathering and Preparing Text, Numbers, and Images Gathering and Preparing Multimedia Elements Assembling the Pages Dynamic generation of web pages. Testing and Posting the Site

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core Text Books  Lengel, James G., "Web wizard's guide to web design," Addison Wesley, 2002 Recommended Books  Nielsen, Jacob, "Designing Web Usability", New Riders Publishing, 2000  Williams, Robin, "The Non-Designer's Design Book," Peachpit Press, 2004  Price, Michael, "FrontPage 2003 in easy steps," Computer Step, 2004  Hester, Nolan, "Microsoft Office FrontPage 2003 for Windows," Peachpit Press, 2004  Meyer, Eric A., "Cascading Style Sheets: the definitive guide," O'Reilly, 2000  Ullman, Larry,"PHP for the World Wide Web," Peachpit Press, 2004  Butzon, Toby, "PHP by example," QUE publishing, 2002  Lash, David A., "Web wizard's guide to PHP," Addison Wesley, 2003  Hughes, Cheryl M., "Web wizard's guide to XHTML," Addison Wesley, 2005  Castro, Elizabeth, "HTML, XHTML & CSS: visual quickstart guide," Peachpit Press, 2007

234

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

235

MODULE SPECIFICATION

Module title

Management Information Systems

Module code

IS 413

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 321 Database Management Systems 1

Co-requisites

None

Excluded combinations

None

236

LEARNING OUTCOMES Knowledge and understanding   

The field of information management at the level of theory, principles, contemporary practice and application. Current practice, issues and opportunities in information management practice with critical understanding of the key professional, ethical, legal, social, technological, quality and service perspectives. Analyzing complex issues within the application and practice of information management and identify with problems and their solution requiring synthesis of inputs, management of processes and evaluation of outcomes.

Subject specific skills (including practical/professional skills)    

Create and develop a computerised Management Information System using a relational database application, which produces relevant operational, tactical and strategic reports Model information flows within organisations Formulate appropriate strategies in order to secure the data and equipment of an Information System Explain the potential impact of information systems on organisational efficiency, effectiveness, strategic advantage and strategic re-structuring

Cognitive skills    

Understand the value of technology in information management and management techniques and be competent to apply them in appropriate ways Operate autonomously as an information manager, taking personal responsibility, communicating effectively, showing leadership and responding fully to professional needs alone and in a group. Discuss information systems planning and management approaches and requirements in organisations Discuss information systems management structures in complex organisations

Key transferable skills      

Committed and competent to define, provide and evaluate a quality service that is appropriate to its context. Develop problem – solving skills; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Acquire a reasonable facility in numerical and symbolic calculation; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

237

INDICATIVE CONTENT                

Managing the Digital Firm Information Systems in the Enterprise Information Systems: Organizations, Management, and Strategy The Digital Firm: Electronic Business and Electronic Commerce Ethical and Social Issues in the Digital Firm IT Infrastructure and Platforms Managing Data Resources Telecommunications, Networks and the Internet The Wireless Revolution Information Systems Security and Control Enterprise Applications and Business Process Integration Managing Knowledge in the Digital Firm Enhancing Decision Making in the Digital Firm Redesigning the Organization with Information Systems Understanding the Business Value of Systems and Managing Change Managing Global Systems

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core text  James A. O'Brien, George Marakas, "Management Information Systems," 7th edition, McGraw-Hill/Irwin, January 14, 2005 Supplementary reading  Stephen Haag, Maeve Cummings, and Donald J. McCubbrey, "Management Information Systems for the Information Age," 4th edition, McGraw-Hill Companies, Jan 2003  I Barbara C. McNurlin and Ralph H. Sprague, "Information Systems Management in Practice," 7th Edition, Prentice Hall, Feb 1, 2005  Kenneth C. Laudon and Jane P. Laudon, "Management Information Systems: Managing the Digital Firm," 9th Edition, Prentice Hall, Mar 1, 2005

238

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

239

MODULE SPECIFICATION

Module title

Analysis and Design of Information Systems 2

Module code

IS 418

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 312 Analysis & Design of Information Systems 1

Co-requisites

None

Excluded combinations

None

240

LEARNING OUTCOMES Knowledge and understanding     

The design goals to be followed to produce a high quality Object Oriented design for a system. The importance of analysis and design activity in the software development process The stages that a group of people go through to become an integrated team The planning and development of a software product The documentation and testing of a software project.

Subject specific skills (including practical/professional skills)     

Using a CASE tool and the UML notation to record the analysis and design of a software system Identifying and design software components for integration into an existing software system. Drafting a professional test plan for the integration of a software system Producing professional requirements, analysis, and specification documents for a software system Applying apply common design methodology and specific design patterns to a problem

Cognitive skills   

Conduct an Object Oriented Analysis exercise as part of a small group of developers Produce an object oriented analysis of a system and transform it into an appropriate set of software components using appropriate design methodology Plan the integration and testing for a software system

Key transferable skills   



Recognize the inter-personal dynamics that occur within a small project team Organize and conduct formal meetings Plan a project involving four or five people over three to six months Make and use time estimates for activities within a project

241

INDICATIVE CONTENT             

Introduction to System Analysis And Design Introduction to Object-Oriented Systems Analysis and Design with Unified Modelling Languages. Project Initiation Project Management Requirements Determination Functional Modelling Structural Modelling Behavioural Modelling Class And Method Design Data Management Layer Design Human Computer Interaction Layer Design Physical Architecture Layer Design Installation And Operations

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Alan Dennis, Barbara Haley Wixom, and David Tegarden , "Systems Analysis and Design with UML Version 2.0: An Object-Oriented Approach ," 2nd edition, Wiley, Aug 10, 2004 Supplementary reading  Simon Bennett, Steve McRobb, and Ray Farmer ," Object-oriented Systems Analysis and Design Using UML," McGraw-Hill Professional, Aug 1, 2005  Mike O'Docherty, "Object-Oriented Analysis and Design: Understanding System Development with UML 2.0 ,"Wiley, Jun 13, 2005  Charles S. Wasson," System Analysis, Design, and Development: Concepts, Principles, and Practices (Wiley Series in Systems Engineering and Management)," WileyInterscience, Dec 23, 2005, Introduction to, Stephen R. Schach , Object-Oriented Analysis and Design Irwin/McGraw-Hill, Jul 11, 2003

242

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

243

MODULE SPECIFICATION

Module title

Object Oriented Database

Module code

IS 421

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 322 Database Management Systems 2

Co-requisites

None

Excluded combinations

None

244

LEARNING OUTCOMES Knowledge and understanding          

Application of advanced data modelling concepts in the creation of an Enhanced Entity Relationship (EER) diagram during the conceptual design of a database application. Application of object-oriented data modelling concepts Assessment of the similarities and differences between different modelling techniques Generation of relational database designs Explanation of the fundamental concepts of object-oriented database systems Formulation of queries using the Object Query Language Mapping conceptual designs expressed in EER and/or UML form Formulation of queries and express data manipulation commands Mapping conceptual database designs expressed in EER and/or UML form to objectrelational schemas using the object model of the SQL standard. Comparison and contrast of object-oriented database technology with object-relational database technology.

Subject specific skills (including practical/professional skills)  

Discuss and present alternative solutions to assigned projects and exercises involving advanced data modelling, object-oriented database systems, object-relational database systems, Web access to databases, and XML. Develop technical documentation describing solutions to assigned projects and exercises involving advanced data modelling, object-oriented database systems, objectrelational database systems, Web access to databases, and XML.

Cognitive skills     

Develop a schema for an object-oriented database application using the Object Definition Language of the Object Data Management Group Standard. Demonstrate the use of object-oriented database concepts using a commercial objectoriented database system. Describe the fundamental concepts of object-relational database technology, such as abstract data types, object identity, object references, inheritance, and the use of collection types. Develop a schema for an object-relational database application using the objectrelational model of the SQL standard. Demonstrate the use of object-relational concepts using a commercial object-relational database system.

Key transferable skills   

Work together with a team to specify, design, analyze, implement, and document a Web-accessible database application. Communicate effectively in team meetings about database issues. Use different means of communication for interacting with team members about database issues.

245

INDICATIVE CONTENT 





 

Object-Oriented Database Systems (OODBMS) - The Object-Oriented Data Model - Complex Types and Object-Orientation in Database Systems - Query Processing in OODBMS - Storage Structures for OODBMS - The ODMG Standard Object-Relational Database Systems - Object-Relational Data Models - User-Defined Types and Functions - Query Processing - Object-Relational System Architectures - The SQL3 standard Distributed Database Systems - Concepts of Distributed Databases - Distributed Database Design - Distributed Query Processing and Transaction Management Other Types of Database Systems Advanced Database Applications - Database Connectivity: ODBC and JDBC - Querying Databases through the Web - Multi-database Systems and Database Integration - Data Warehouses, OLAP and Data Mining

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. INDICATIVE SOURCES Core Text Books  Silberschatz, H.F. Korth, and S. Sudarshan: Database System Concepts, (5th Edition), McGraw-Hill, 2006 Recommended Books  Jan L. Harrington, Object-Oriented Database Design Clearly Explained 1st edition, Morgan Kaufmann, Oct 7, 1999  Michael R Blaha and William Premerlani, Object-Oriented Modelling and Design for Database Applications, Prentice Hall, Jul 17, 1997  Won Kim, Introduction to Object-Oriented Databases (Computer Systems Series), MIT Press, 1992 246

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element A 3-hour unseen examination. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element Midterm class test. Assignments and quizzes Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

247

MODULE SPECIFICATION

Module title

Data Storage and Retrieval

Module code

IS 432

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 321 Database Management Systems 1

Co-requisites

None

Excluded combinations

None

248

LEARNING OUTCOMES Knowledge and understanding        

Sequential file access for storing and retrieving disk data. Direct file access for storing and retrieving disk data. The use of file organization methods to improve file access efficiency. Primary and secondary keys to store and retrieve data. Indexes to improve retrieval of data. Consequential processing techniques. The structure of B-trees and B+trees to index data for keyed access. Hashing techniques to store and retrieve data by key.

Subject specific skills (including practical/professional skills)        

Use sequential file access to store and retrieve disk data. Use direct file access to store and retrieve disk data. Use file organization methods to improve file access efficiency. Use primary and secondary keys to store and retrieve data. Use indexes to improve retrieval of data. Use consequential processing techniques. Use B-trees and B+trees to index data for keyed access. Use hashing techniques to store and retrieve data by key.

Cognitive skills       

Synthesis and evaluation of the technical concepts of the Contents. Appraisal of theory and its relevance to different situations. Analysis of tasks into understandable and manageable subtasks. Synthesis of clearly and precisely stated solutions for problems. The testing of proposed solutions for validity. Correction of and refinement of proposed solutions. Evaluation of proposed solutions for scale and scalability.

Key transferable skills         

Organize data into usable information. Use the object oriented programming paradigm and use the C++ programming language to solve general computational problems. Synthesize clearly and precisely stated solutions for problems. Test proposed problem solutions for validity and usability. Correct and refine proposed solutions. Design for the effects of scale and scalability on solution choices Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working. 249

INDICATIVE CONTENT              

Introduction Sequential File Organization Direct File Organization Indexed Sequential File Organization Bits of Information Secondary Key Retrieval Bits and Hashing Binary Tree Structures B-Trees and Derivatives Hashing Techniques for Expandable Files Other Tree Structures Secondary Key Retrieval Revisited Sorting Applying File Structures

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Alan L. Tharp, "File Organization and Processing", Wiley, January 1, 2001 Supplementary reading  Michael J. Folk, Bill Zoellick, and Greg Riccardi , "File Structures: An Object-Oriented Approach with C++ 1st edition", Addison Wesley, December 16, 1997  Michael J. Folk , Bill Zoellick ,"File Structures 2’nd edition" ,Addison Wesley Publishing Company,1992  Panos E. Livadas, "File Structures: Theory and Practice", Prentice Hall, March 1990

250

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

251

MODULE SPECIFICATION

Module title

Decision Support Systems and Applications

Module code

IS 441

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 341 Modelling and Simulation

Co-requisites

None

Excluded combinations

None

252

LEARNING OUTCOMES Knowledge and understanding     

Achieving an understanding of the decision-making and problem solving processes and models for business and management Reviewing and clarifying the fundamental terms, concepts and theories associated with Decision Support Systems, computerized decision aids, expert systems, group support systems and executive information systems. Identifying and analyse business and management problems for potential decision support applications with clear business awareness Having knowledge of using expert systems, web-based expert systems, fuzzy logic, intelligent agents and hybrid systems to support decision making and problem solving for business, e-business and management. Examining examples and case studies documenting computer support for organizational decision making, and various planning, analysis and control tasks.

Subject specific skills (including practical/professional skills)   

Discussing and developing skills in the analysis, design and implementation of computerized Decision Support Systems. Examining user interface design issues and evaluate the user interfaces and capabilities of Decision Support Systems. Improving hands-on skills using HTML, Microsoft Access and Excel, and JavaScript for building state-of-the-art Decision Support Systems, especially Web-Based systems that use advanced computing and networking technologies.

Cognitive skills  

Understand that most Decision Support Systems are designed to support rather than replace decision makers and the consequences of this perspective for designing DSS. Discuss organizational and social implications of Decision Support Systems.

Key transferable skills    

Solve problems effectively. Collaborate with others in a small group to solve a common problem. Plan technical activities more accurately Manage and organize time.

253

INDICATIVE CONTENT  Decision making and decision support systems for business and management - Decision making and problem solving processes and models - The concepts of decision support systems; - Structure and functional components of a decision - Support system and Application examples of decision support systems for business and management.  Decision support using spreadsheets - Data, models, and user interfaces of a decision support application; - Decision making and decision support techniques and models for business and management such as statistical - Analysis, forecasting analysis, decision trees, simulation analysis, risk analysis, what-if analysis, sensitivity analysis, goal seeking, cash flow analysis, etc.  Using group decision support systems  Using expert systems to support decision-making for business and management - Concepts and functional components of expert systems; - Web-based expert systems; - Strengths and weaknesses of expert systems; software demonstration application  Using fuzzy logic, intelligent agents, hybrid systems to support decision-making TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Turban, E., Aronson, J. and Liang, T. (2005), Decision Support Systems and Intelligent Systems (7th edition), Prentice Hall International. Supplementary reading  Barlow, J. F. (2001), Excel Models for Business and Operations Management, John Wiley& Sons.  Edwards, J. S. and Finlay, P. (1997), Decision Making with Computers, Pearson Education.

254

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

255

MODULE SPECIFICATION

Module title

Linear and Integer Programming

Module code

IS 442

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 342 Intro to Operations Research & Decision Support Systems

Co-requisites

None

Excluded combinations

None

256

LEARNING OUTCOMES Knowledge and understanding     

The basic ideas underlying optimization techniques. Some of the most common standard optimization models and how they can be solved. Formulating problems as mathematical programs and solving them; Identifying problem situations in which mathematical programming should (or should not) be considered; Carrying out sensitivity analysis to see how robust the recommendation is

Subject specific skills (including practical/professional skills)   

Developing mathematical optimization models for a range of practical problems Formulating large-scale Linear and Integer programming problems, input a problem into a computer efficiently, and then solve the problem Using special methods for transportation and assignment problems

Cognitive skills  

Understand some of the power of using the mathematical approach to optimization problems relevant to engineering. Show logical thinking in problem solving.

Key transferable skills     

Be aware of major heuristic techniques and know when and how to apply them. Develop problem – solving skills; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

257

INDICATIVE CONTENT              

Theory of lattices and linear Diophantine equations. Algorithms for linear Diophantine equations. Diophantine approximation and basis reduction Fundamental concepts and results on polyhedra, linear inequalities, and linear programming The structure of polyhedra. The simplex method Primal-dual, elimination, and relaxation method Khachiyan’s method for linear programming The ellipsoid method for polyhedra Polynomiality results in linear programming Estimates in integer linear programming Totally unimodular matrices: fundamental properties and examples Recognize total unimodularity Integral polyhedra and total dual integrity

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Alexander Schrijver, "Theory of Linear and Integer Programming, "Wiley, Jan 1, 2001 Supplementary reading  Richard Kipp Martin, "Large Scale Linear and Integer Optimization: A Unified Approach, "Springer, Jan 1, 2001  Gerard Sierksma ,"Linear & Integer Programming: Theory and Practice (Pure and Applied Mathematics)," 2nd edition, CRC, Jan 15, 2001  John Edward Beasley, "Advances in Linear and Integer Programming (Oxford Lecture Series in Mathematics and Its Applications, 4)," Oxford University Press, Aug 20, 2002  Chvatal, V., "Linear Programming, Freeman,"Sep 15, 1983  Williams, H.P., "Model Building in Mathematical Programming," Wiley, October 14, 1999

258

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

259

MODULE SPECIFICATION

Module title

Decision Support Tools and Techniques

Module code

IS 443

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 341 Modeling and Simulation

Co-requisites

None

Excluded combinations

None

260

LEARNING OUTCOMES Knowledge and understanding    

Models of decision making Decision techniques Implications of the above in terms of IS requirements definition Examining examples and case studies documenting computer support for organizational decision making, and various planning, analysis and control tasks.

Subject specific skills (including practical/professional skills)   

Decision trees Designing Advanced spreadsheet Modelling Dynamic systems

Cognitive skills  

Aware of the context of business decision making. Aware of characteristics of decision making problems and the impact on the approach selected.

Key transferable skills    

Solve problems effectively. Collaborate with others in a small group to solve a common problem. Plan technical activities more accurately Manage and organize time.

261

INDICATIVE CONTENT            

Statistical techniques Baye’s theorem Data gathering and analysis Confidence and errors Naturalistic decision making Pennington and Hastie Decision making under uncertainly Group decision making Delphi and Monte Carlo techniques Game theory Risk analysis Data analysis and business modelling

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core Text Books  Mike Wisniewski, Quantitative Methods for Decision Makers, 4th edition, Prentice Hall, 2006 Recommended Books  Wayne L Winston, Microsoft Excel – Data Analysis and Business Modelling, Microsoft Press, 2004  Gary Klein, The Power of Intuition, Doubleday, 2004  Barlow, J. F. (2001), Excel Models for Business and Operations Management, John Wiley& Sons.  Edwards, J. S. and Finlay, P. (1997), Decision Making with Computers, Pearson Education.

262

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

263

MODULE SPECIFICATION

Module title

Project

Module code

IS 492

Level

6

Module leader

University of Wales credit rating

20

ECTS credit rating

10

Module type

Double

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

None

Co-requisites

None

Excluded combinations

None

264

LEARNING OUTCOMES Knowledge and understanding    

Have completed a substantial piece of work, under the direction of a supervisor but demonstrating self-discipline, organization and initiative; Have demonstrated an ability to gain expertise in a particular area of study largely through directed study; Have produced a critical appraisal of their work, evaluating all aspects of their approach; Be practiced in giving industrial quality poster presentations.

Subject specific skills (including practical/professional skills) 

Demonstrate a range of technical skills in the computer-based processing of data and information in the field of the project.

Cognitive skills   

Identify success criteria, evaluate alternative solutions and make design choices; Conduct literature surveys and collect, manage, analyze and evaluate data related to a concrete information processing application; Take a structured approach to the execution of a research or development project in Computing or IT, employing a number of stages in the analysis of the problem and the synthesis of a solution.

Key transferable skills      

Efficiently manage time when working on a project; Communicate project issues, ideas and progress to others with a general grounding in IT, by means of written reports, formal verbal/visual presentations or practical demonstrations. Develop problem – solving skills; Present, discuss and defend ideas, concepts and views effectively through formal and informal written language; Work co-operatively in a group and share decision making; Develop time management and organizational skills as evidence by the ability to plan and implement efficient and effective modes of working.

265

INDICATIVE CONTENT    

Overview of final year project – procedures and deadlines; Evaluating a project; Writing up a successful project report; Presenting a project.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice on the project, providing general instructions and guidance on how to identify a suitable project, write a project proposal, manage a project, write a project report and prepare presentation material. Tutorial sessions will be follow-up and review sessions, giving consultations on specific problems as they arise and on completing project tasks, individually or in small groups. Laboratory sessions will give students guidance on practical issues relevant to the project, allowing for quick feedback on students progress and understanding. Attendance at lectures, tutorials and laboratory will be regarded as compulsory.

INDICATIVE SOURCES Sources will be dependent upon the project topic.

266

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. Written report 2. Presentation and oral defence.

Element weighting 70% 30%

Component B Description of each element 1. Periodical review sessions 2. Gathering information 3. Project implementation

Element weighting 50% 20% 30%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. Written report 2. Presentation and oral defence.

Element weighting 70% 30%

Component B Description of each element 1. Periodical review sessions 2. Gathering information 3. Project implementation

Element weighting 50% 20% 30%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

267

MODULE SPECIFICATION

Module title

Internet Information Systems

Module code

IS 311

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

CS 362 Internet Technologies

Co-requisites

None

Excluded combinations

None

268

LEARNING OUTCOMES Knowledge and understanding      

Information architecture principles for websites Client-side and server-side interactivity techniques Principles of web scripting languages Website – Data Base connection techniques Website maintenance techniques Web security and privacy issues.

Subject specific skills (including practical/professional skills)    

Construct websites using HTML and authoring tools Implement client-side interactivity using JavaScript. Implement server-side interactivity using ASP. Dynamically generate web pages from a database table.

Cognitive skills  

Ability to use knowledge and understanding of appropriate principles to design information-rich websites. Ability to critically evaluate website designs

Key transferable skills     

Solve problems effectively. Collaborate with others in a small group to solve a common problem. Express ideas in writing, implementing and orally. Evaluate alternatives Manage and organize time.

269

INDICATIVE CONTENT 









Representations of data on the Web o Review of Hypertext Markup o Language (HTML) o Extensible Markup Language o (XML) o Semi-structured data Languages for typing Web data o Document Type Definitions o (DTDs) o XML Schema Definition Language Languages for querying and transforming Web data o XML Path Language (XPath) o Extensible Stylesheet Language o (XSL) o XML query language (XQuery) Review of the Internet o Associated protocols (TCP/IP, o HTTP) o Client-side processing o Scripting with JavaScript o Document Object Model (DOM) Server-side processing o Gateway Interface (CGI) o Java Server Pages (JSP) o Active Server Pages (ASP)

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Alexander Nakhimovsky and Tom Myers, “XML Programming: Web Applications and Web Services with JSP and ASP,” Apress, 2002. Supplementary reading  Goldfarb, Charles F. and Prescod, Paul, “Charles F. Goldfarb's XML Handbook,” Fourth Edition, Prentice-Hall, 2002. 270

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

271

MODULE SPECIFICATION

Module title

Data Warehousing

Module code

IS 331

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 321 Database Management Systems 1

Co-requisites

None

Excluded combinations

None

272

LEARNING OUTCOMES Knowledge and understanding    

Identifying the steps needed to prepare data for a data warehouse, including data cleansing and data modelling Critically appraising an information system and distinguish between the need for a traditional database system or a data warehouse and then model a data warehouse Understanding the setting up, operation and management of a data warehouse, noting its system development life cycle, and how all these aspects differ from OLTP (on line transaction processing). Critically appraising existing data warehouses, and identifies good theoretical and practical elements.

Subject specific skills (including practical/professional skills)      

Being familiar with using the data warehousing and data mining functionality of various types of Data Base Management System. Having a good understanding of database systems in general and relational database systems in particular. Being capable of designing database applications of reasonable complexity using modern software engineering principles. Being capable of implementing a database system using a commercial database management system. Being aware of recent developments in database technology. Being able to interface a relational database to the web.

Cognitive skills   

Analyse problems amenable to data warehousing and data mining approaches and identify appropriate technical solutions. Use such knowledge and understanding in the design of computer-based systems for Data Warehousing and/or Data Mining, appropriate to the requirements of a specific application Recognize and tackle the problems of distributed databases, noise, missing or nonsense data, in implementing Data Mining solutions

Key transferable skills     

Solve problems effectively. Collaborate with others in a small group to solve a common problem. Demonstrate effective use of general IT facilities Demonstrate management of their own learning and development including time management and organizational skills. Communicate effectively using appropriate interpersonal skills and using different media.

273

INDICATIVE CONTENT        

Data Warehousing Concepts. Data Warehouse Architecture Data Warehouse Data Flows Data Warehouse Tools and Technologies Designing a Data Warehouse Database Online Analytical Processing (OLAP) and Data Mining Data Mining Techniques Grids

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Matthias Jarke, Maurizio Lenzerini, Yannis Vassiliou, and Panos Vassiliadis, “Fundamentals of Data Warehouses,” Hardcover 2003  Kroenke, D, “Database Processing,” 8th Edition, Prentice Hall 2002. Supplementary reading  M. Jarke et al, “Fundamentals of Data Warehouses” 2nd Edition, Springer 2003  C. Seidman, “Data Mining with Microsoft SQL Server 2000,” Technical Reference Microsoft Press  J. Han, M Kamber, “Data Mining Concepts and Techniques,” Morgan Kaufmann 2001

274

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

275

MODULE SPECIFICATION

Module title

Advanced Modelling and Simulation

Module code

IS 345

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 341 Modelling & Simulation

Co-requisites

None

Excluded combinations

None

276

LEARNING OUTCOMES Knowledge and understanding     

Realizing the importance of conceptual modelling Identifying and developing the core skills required for the successful application of simulation techniques Developing models for business process reengineering Specifying and demonstrating a computer simulation for business process modeling Applying statistical and analytical techniques for simulation experimentation.

Subject specific skills (including practical/professional skills)          

Design and implement a simple computational model, and analyse its output with the help of basic statistical techniques Develop software programs for complicated mixed time-and-event-driven systems. Achieve the system modelling of basic engineering systems Design a basic adaptive learning system for engineering problems Critically appraise a design and the tools necessary for its complete analysis Systematically approach the solution of design problems Solve real design and engineering problem using a combination of modern simulation and experimental mechanics tools Mathematically model a problem by utilising the correct equation set and choosing the appropriate engineering constraints Set up a problem numerically through pre-processing, processing and post-processing stages Implement any additional physics or experiments required for validating numerical solutions

Cognitive skills             

Demonstrate an appreciation of the range of simulation techniques available, and of the special issues involved in modelling complex adaptive systems Justify decisions made when designing a model, implementing a model, and analyzing a model Develop mathematical models for both time-driven and event-driven systems. Model, simulate, and validate random processes. Design simulation programs for particularly specified systems. Understand the methods of system optimisation and adaptive control design. Pursue the further study by themselves in this subject and relevant areas. Contribute as competent and effective member of an engineering team Have the capability to take an idea from its conceptual stage and develop it to a fully functioning simulation using one or a combination of analytical modelling, computer simulation and experimental methods Analyze and interpret the simulation results leading to the total solution and realization Evaluate methodologies and formulate a solution strategy Critically appraise and filter information to achieve the desired aim and objective Perform qualitative and quantitative assessment of the solutions 277

Key transferable skills      

Solving problems through modelling and Simulation. Collaborating with others in a small group to solve a common problem. Demonstrating effective use of general IT facilities Demonstrating management of their own learning and development including time management and organizational skills. Communicating effectively using appropriate interpersonal skills and using different media. Developing independent learning, problem solving and design skills

INDICATIVE CONTENT      

The Role of Operational Research Queuing Theory Design and Analysis of Simulation Models Linear Programming Inventory Control Critical Path Analysis

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Bernard P. Zeigler, Tag Gon Kim, and Herbert Praehofer, “Theory of Modelling and Simulation,” Hardcover , 2000 Supplementary reading  Pidd M, "Computer Simulation in Management Science," John Wiley, 1998.  Oakshott L, "Business Modelling and Simulation," Pitman Publishing, 1997.  Harrington. HJ, "Simulation Modelling Methods," McGrawhill, 2002.

278

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

279

MODULE SPECIFICATION

Module title

E-commerce

Module code

IS 352

Level

5

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

CS 362 Internet Technologies

Co-requisites

CS 362 Internet Technologies

Excluded combinations

None

280

LEARNING OUTCOMES Knowledge and understanding       

Defining E-commerce and e-business What E-commerce is and how it is distinct from E-Business and discuss various forms and levels of information about them. Practical and commercial issues for introducing E-commerce and e-business ideas into a (distributed) organization Technical issues for implementing an e-system and some of the commonly available technology components available Discussing and explaining theoretical and practical issues of conducting business over the internet and the Web Demonstrating the ability to implement a simple web based e-system Evaluating user needs

Subject specific skills (including practical/professional skills)        

Making decisions based on best current practice relating to the design of web sites for E-commerce Designing and usability skills Logical, well-structured and coherent presentation, geared to answer the question asked The awareness of not only the broad range of E-commerce business issues but also development of practical applications skills to support E-commerce activity by use of the latest E-commerce enabling software. Comparing and contrasting traditional marketing methods with those available through using the Internet Developing an integrated marketing strategy for organisations, incorporating the Internet and E-commerce. Research of a market / industry and marketing theory from a variety of sources, credited, with full bibliography Applying appropriate marketing theory to the analysis of an e market

Cognitive skills     

The ability to define, document and manage user needs for e – business The ability to reflect on general principles revealed through practical exploration of specific tools, techniques and methods in e -business. The ability to analyse e -commerce situations shown by insight into the market situation and the application of appropriate theory The ability to evaluate situations, provide solutions with explanation to problems identified and consider alternatives in relation to appropriate criteria and make management recommendations Assessing the broader social, political and economic factors that shape, and are shaped by, E-commerce

281

Key transferable skills    

Solve problems effectively. Collaborate with others in a small group to solve a common problem. Demonstrate effective use of general IT facilities Demonstrate management of their own learning and development including time management and organizational skills. Communicate effectively using appropriate interpersonal skills and using different media.



INDICATIVE CONTENT 

 





Introduction to E-commerce - The Revolution Is Just Beginning - E-commerce Business Models and Concepts Technology Infrastructure for E-commerce. The Internet and World Wide Web: - E-commerce Infrastructure - Building an E-commerce Web Site - Security and Encryption - E-commerce Payment Systems Business Concepts and Social Issues - E-commerce Marketing Concepts - E-commerce Marketing - Communications - Social, Legal and Ethical Issues In E- Commerce E-commerce in Action. - Retailing on the Web - Online Service Industries - Digital Media - Auctions, Portals and Communities - B2b E-commerce - Supply Chain Management and - Collaborative Commerce

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding. 282

INDICATIVE SOURCES Core text  Kenneth Laudon, Carol Traver, “E-commerce Business, Technology, Society” 3rd Edition, Hardcover 2006. Supplementary reading  Don Jones, Mark Scott, and Rick Villars, “E-commerce for Dummies,” Paperback 2001.  Chaffey, Dave “E-Business and E-commerce Management,” Prentice Hall 2002.  Janice Reynolds “The Complete E-commerce Book: Design, Build and Maintain a Successful Web-Based Business,” Paperback 2004.

283

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

284

MODULE SPECIFICATION

Module title

Information Systems Development Methodologies

Module code

IS 411

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 312 Analysis & Design of Information Systems 1

Co-requisites

None

Excluded combinations

None

285

LEARNING OUTCOMES Knowledge and understanding         

The systemic/holistic approach to the development process The different approaches to systems development (formal, semi-formal, informal), soft, hard, participative, socio-technical, and their relative strengths The breakdown of the systems development stages as interpreted in various methodologies Importance of the need of methodologies Problems which the software industry currently faces Approaches to solving these problems Awareness of how complex information systems are Awareness of some of the techniques used to build information systems and their applications The difference between hard and soft methodologies

Subject specific skills (including practical/professional skills)    

Supporting programming elements in the form of different notations used in methodologies; Understanding and applying the principles, philosophies, practices, techniques and tools of various methodologies. Making use of available automated CASE tools for the creation and validation of the models Using a research-based approach through the comparative method and the use of stateof-the-art study material (research papers, journals and recent publications)

Cognitive skills        

An understanding of abstraction found in modelling information systems Ability to use prior knowledge of one methodology and reasoning about other methodologies Ability to link common features in methodologies Ability to identify overlapping concepts in methodologies. Developing a critical view of taxonomies of methodologies proposed to-date Contributing to the work of a group working on applying the techniques of a specified methodology to a real-life problem of an appropriate domain Organizing and present written and oral technical material in a professional manner Evaluating peers through the active participation in presentations and their assessment

Key transferable skills     

Critical reasoning Skills in presenting papers/publications Awareness of the difficulties/impossibility of finding a perfect solution to a development problem Being able to make valuable judgments on new methodologies, not just those covered in the unit Awareness of the need for frameworks as a way of evaluating a methodology 286

  

Contributing to a group working on applying the techniques of a specified methodology to a real-life problem of an appropriate domain; Organizing and present written and oral technical material in a professional manner Evaluating peers through the active participation in presentations and their assessment

INDICATIVE CONTENT                 

Introduction and definitions; Analysing and Problem Solving; CASE in Systems Development Process; The Software Life Cycle; Decision Process; Information Systems Projects and Cost-Benefit Analysis; Managing Information Systems Projects; Information Gathering; Process Modelling and Logic Modelling; E-R Diagram; Object-Oriented Analysis and Design; Unified Modelling Language; Alternative design strategies; SDLC Design Considerations; Implementing and testing Project Management, Quality Management, and Configuration Management; Engineering Standards.

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Marite Kirikova, Janis Grundspenkis, Wita Wojtkowski, and W. Gregory Wojtkowski, "Information Systems Development: Advances in Methodologies, Components and Management," 1st edition, Springer, Feb 28, 2003 Supplementary reading  David Avison, "Information Systems Development," McGraw-Hill College, Mar 1, 2006  D.E. Avison and G. Fitzgerald, "Information Systems Development (Information Systems Series)," 3rd edition, McGraw Hill Higher Education, Sep 1, 2002 287

 

Charles S. Wasson, "System Analysis, Design, and Development: Concepts, Principles, and Practices," Wiley-Interscience, Dec 23, 2005 Hari Harindranath, W. Gregory Wojtkowski, Joze Zupancic, and Duska Rosenberg," New Perspectives on Information Systems Development: Theory, Methods and Practice," 1st edition, Springer, Sep 26, 2002

288

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

289

MODULE SPECIFICATION

Module title

Intelligent Information Systems

Module code

IS 412

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

CS 431 Artificial Intelligence

Co-requisites

None

Excluded combinations

None

290

LEARNING OUTCOMES Knowledge and understanding      

The Categorisation of information systems and identify their applications in industry. The Critical analysis of the merits and shortfalls of expert systems, fuzzy logic and its applications, neural networks and knowledge discovery, and evolutionary computing. Applying optimisation concepts, approaches and tools in real-life situations. Solving industrial problems using soft computing techniques. Demonstrating a systematic understanding of data management techniques. Identifying opportunities in a business where data management techniques can add value.

Subject specific skills (including practical/professional skills)  

Using appropriate tools for problems in Intelligent Information Systems. Developing software solutions to practical problems

Cognitive skills     

Critically Analyse problems relating to Intelligent Information Systems Identification of appropriate technical solutions. Demonstrating the application of knowledge capture techniques. Evaluating technologies in context. Evaluating current limitations of techniques in Intelligent Information Systems.

Key transferable skills     

Learn in both familiar and unfamiliar situations making effective use of informationretrieval skills and of learning resources Communicate effectively using various media and with a variety of audiences Use general Information Technology facilities effectively. Demonstrate self-direction and originality in tackling and solving problems, and act autonomously in planning and implementing tasks to a professional level. Appreciate the need for continuing professional development in recognition of the requirement for Life Long Learning

291

INDICATIVE CONTENT   

     

Business and information Types of information systems and their applications Intelligent information systems - Technical challenges - Knowledge capture techniques - Expert systems - Fuzzy logic and its applications Neural networks and knowledge discovery Optimisation concepts: approached and tools Evolutionary computing Industrial applications of soft computing and case studies Data quality management Data management process

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Ronald G. Anderson "Information and knowledge based systems, an introduction" Prentice Hall 1992. Supplementary reading  John F. Sowa "Knowledge Representation" Brokks/Cole 2000  David E. Rumelhart "Introduction to human information processing" John Whiley and Sons, 1977

292

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

293

MODULE SPECIFICATION

Module title

Geographical Information Systems

Module code

IS 416

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 322 Database Management Systems 2

Co-requisites

None

Excluded combinations

None

294

LEARNING OUTCOMES Knowledge and understanding    

The fundamentals of GIS. Sources of spatial environmental data and methods of analysing and combining them. The problems and limitations associated with the use of GIS. The use of GIS as a tool for research and management and to support decision making.

Subject specific skills (including practical/professional skills)   

Using a GIS to inform research and environmental management decision making Designing and evaluating the quality of environmental data. Assessing when the use of a GIS is, and is not, appropriate for a particular application.

Cognitive skills   

The ability to appreciate the limitations of spatial data and how these may be mitigated The ability to synthesise complex and sometimes contradictory information The ability to interpret complex data.

Key transferable skills    

Work effectively as part of a small team and individually Plan and manage their time effectively Communicate effectively in written and oral form Manipulate and analyse complex data sets

INDICATIVE CONTENT             

What is GIS? Spatial data Spatial data modelling Database management Data input and editing Data analysis Analytical modelling in GIS Output: from new maps to enhanced decisions The development of computer methods for handling spatial data Data quality issues Human and organizational issues GIS project design and management The Future of GIS

295

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups. Laboratory sessions will provide students with assistance to apply theoretical concepts learned in lectures and complete practical exercises. Attendance at lectures, tutorials and laboratory will be regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Ian Heywood, Sarah Cornelius, Steve Carver, “An Introduction to Geographical Information Systems,” 3rd Edition, Prentice Hall 2006. Supplementary reading  Longley, P., Goodchild, M., Maguire, D. and Rhind, D. “Geographic Information Systems and Science,” Second Edition, Wiley 2005. 

Delaney, J. “Geographical Information Systems: An Introduction,” OUP, Oxford 1999.



Wise, S. “GIS Basics,” Routledge, 2002

296

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination. 2. A Practical Laboratory examination

Element weighting 83.3% 16.7%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes 3. Laboratory reports

Element weighting 50% 25% 25%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

297

MODULE SPECIFICATION

Module title

Data Mining

Module code

IS 431

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 321 Database Management Systems 1

Co-requisites

None

Excluded combinations

None

298

LEARNING OUTCOMES Knowledge and understanding     

The explanation of the differences between the major data mining tasks, in terms of their assumptions, requirement for a specific kind of data, and the different kinds of knowledge discovered by algorithms performing different kinds of task; The description of the state-of-the-art data mining algorithms for the major data mining tasks; The identification of which data mining task and which algorithm is the most appropriate for a given data mining project, taking into account both the nature of the data to be mined and the goals of the user of the discovered knowledge; The use of a state-of-the-art data mining tool in a principled fashion, being aware of the strengths and weaknesses of the algorithms implemented in the tool; The main tasks and state-of-the-art algorithms involved in the pre-processing and postprocessing steps of the knowledge discovery process.

Subject specific skills (including practical/professional skills)      

Recognise and evaluate the validity of real-world data for data mining; Evaluate the quality of discovered knowledge, taking into account the requirements of the data mining task being solved and the business goals of the user; Extend data mining concepts and principles to text and web mining; Utilize the library and exploit web sites to support investigations into these areas; Recommend strategies and techniques based on the data mining context; Evaluate and analyse the results of the data mining process;

Cognitive skills   

Understand the major kinds of data mining tasks and the main kinds of algorithms that are often used to solve these tasks; Understand the strengths and weaknesses of some data mining algorithms, identifying the kind of algorithm that is most appropriate for each data mining problem; Understand the process of knowledge discovery, involving not only data mining but also pre-processing and post-processing steps.

Key transferable skills     

Work effectively as part of a small team and individually Plan and manage their time effectively Communicate effectively in written and oral form Problem formulation and decision making Develop IT skills in context

299

INDICATIVE CONTENT   





Overview Basics of vector and metric spaces, probability theory and statistics Analysing Vectorial data - Dimensionality reduction techniques - Clustering techniques - Classification and regression techniques Analysing structured data - Mining textual data - Other structured data types Searching the web

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Oded Maimon and Lior Rokach, “Data Mining and Knowledge Discovery Handbook,” Hardcover 2005.  Margaret Dunham, “Data Mining,” Prentice Hall 2003. Supplementary reading  David J. Hand, Heikki Mannila and Padhraic Smyth, “Principles of Data Mining,” MIT Press 2003.  Trevor Hastie, Robert Tibshirani, and Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” Springer 2001.  Paolo Giudici, “Applied Data Mining: Statistical Methods for Business and Industry,” John Wiley & Sons 2003.

300

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

301

MODULE SPECIFICATION

Module title

Computational Intelligence in Operations Research and Decision Support

Module code

IS 445

Level

6

Module leader

University of Wales credit rating

10

ECTS credit rating

5

Module type

Standard

Owning institution

Modern University for Technology and Information

Field

Computer Information Systems

Valid from

September 2007

Contributes towards

BSc.(Hons) Computer Information Systems

Pre-requisites

IS 342 Intro. to OR & Decision Support Systems

Co-requisites

None

Excluded combinations

None

302

LEARNING OUTCOMES Knowledge and understanding      

The appreciation of the advantages of the neural learning, fuzzy, and evolutionaryrelated approaches to engineering problems, in relation to the traditional methodologies. The familiarity with different techniques, their principal functionality and inner workings, as well as the popular and efficient methodologies. The appreciation of how neural networks, fuzzy logic and evolutionary algorithms can be applied to signal/image processing, pattern classification and recognition, system modelling, prediction, control and optimisation. The appreciation of the advantages of such techniques to complex problem solving in contrast to the traditional methodologies. The familiarity with the genetic encoding and modelling for solving optimisation problems. An understanding of the genetic operators and their performance.

Subject specific skills (including practical/professional skills)      

Using advanced computing techniques for data analysis and visualisation Formulating appropriate representations of problems Clarifying the nature of a 'solution' in the real world. Developing a range of analytical, modelling, problem solving and management consultancy potential Solving complex problem using advanced OR and DS techniques. Using neural networks and fuzzy logic techniques in images processing

Cognitive skills     



Formulate a problem in terms of neural learning, fuzzy or genetic operators (where applicable) and use or derive an appropriate algorithm or strategy for its solution. Pursue further individual study in this and other relevant areas. Analyse and solve some managerial problems in engineering with popular Operational Research methods and techniques Evaluates critically the role of OR within a business context, being aware of both the internal and external forces for change which influence the role Interact effectively within a team/learning/professional group, recognising, supporting or being proactive in leadership, able to negotiate in a professional context and manage conflict. Moreover, engage effectively in debate in a professional manner and produce detailed and coherent reports. Work through problems and making creative and purposeful change and adaptation with an awareness of ethical and moral codes and demonstrating integrity of conduct.

Key transferable skills     

Work effectively as part of a small team and individually Plan and manage their time effectively Communicate effectively in written and oral form Problem formulation and decision making Develop IT skills in context 303

INDICATIVE CONTENT         

Structures of Neural networks Learning Process Applications on neural network and system modelling Evolutionary methods and optimization Evolutionary Optimisation and Genetic Algorithms Evolutionary Programming Particle Swarm Intelligence Applications on pattern recognition and evolved systems Fuzzy Logic Systems

TEACHING AND LEARNING METHODS Lectures will be used to formally introduce the balance of emphasis between theory and practice. Tutorial sessions will give students guidance, individually or in small groups to apply theoretical concepts learned in lectures and complete exercises. Attendance at lectures and tutorials is regarded as compulsory. Students will work on the tutorial example sheets between sessions, these example sheets will be purely formative in nature, peer marked, allowing for quick feedback on students progress and understanding.

INDICATIVE SOURCES Core text  Amit Konar, “Computational Intelligence: Principles, Techniques and Applications,” Hardcover, 2005 Supplementary reading  S. Haykin, “Neural Networks - A comprehensive Foundation," IEEE Press, also Macmillan 1994.  John Yen and Reza Langari, "Fuzzy Logic - Intelligence, Control and information," Prentice Hall, London, 1999

304

ASSESSMENT A: 60% B: 40%

Weighting between components A and B ATTEMPT 1

First Assessment Opportunity Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

Second Assessment Opportunity (further attendance at taught classes is not required) Component A Description of each element 1. A 3-hour unseen examination.

Element weighting 100%

Component B Description of each element 1. Midterm class test. 2. Assignments and quizzes

Element weighting 50% 50%

SECOND (OR SUBSEQUENT) ATTEMPT Attendance at taught classes is required.

Module specification confirmed by ………………………………………………… Date …………………………… (Programme Director)

305