Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Name of Program: MCA (BANKING TECHNOLOGY) TEACHING & EVALUATION SCHEME

60

20

20

0

0

L

T

P

CREDITS

Modeling and Simulation

Teacher Assessment*

COMPULSORY

SUBJECT NAME

End Sem University Exam

MCBT 201

Category

PRACTICAL Teacher Assessment*

SUBJECT CODE

End Sem University Exam Two Term Exam

THEORY

4

1

0

5

Legends: L - Lecture; T - Tutorial/Teacher Guided Student Activity; P – Practical; C - Credit; Q/A – Quiz/Assignment/Attendance, MST - Mid Sem Test. *Teacher Assessment shall be based on following components: Quiz/ Assignment/ Project/ Participation in class (Given that no component shall be exceed 10 Marks) Course Educational Objectives (CEOs):  To develop mathematical models of phenomena involved in various chemical engineering processes and solutions for these models.  To introduce students to basic simulation methods and tools for modelling and simulation of continuous, discrete and combined systems. Course Outcomes (COs):  Understand the important physical phenomena from the problem statement  Develop model equations for the given system  Demonstrate the model solving ability for various processes/unit operations  Demonstrate the ability to use a process simulation Unit-I Introduction to Modeling and Simulation: Concept of Systems, Nature and Concept of Simulation, Steps in simulation study, Models and Simulation, Continuous and discrete system modeling, Model development life cycle, Components of a simulation study, Principles used in modeling system studies, Static and Dynamic physical models, Static and Dynamic Mathematical models Introduction to Static and Dynamic System Simulation, Advantages and Disadvantages of Simulation. Unit-II System Simulation: Techniques of simulation ,Types of System Simulation, Monte Carlo Method, Comparison of Simulation and analytical methods, Numerical Computation techniques for Continuous and Discrete Models, Cobweb Model ,Distributed Lag Models. Continuous System Simulation: Continuous System models, Analog and Hybrid computers, Digital-Analog Simulators, CSSLs, Hybrid simulation, Real Time simulations, and Feedback systems.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Unit –III System Dynamics & Probability concepts in Simulation : Historical background Exponential growth and decay models, modified exponential growth models, logistic curves ,Generalization of growth models, System dynamics diagrams, dynamo language, Multi segment models, Representation of Time Delays, Stochastic variables, Discrete and Continuous probability functions, Continuous Uniform Distributed Random Numbers, Uniform random numbers generator, Generating Discrete distributions, Non-Uniform Continuously Distributed Random Numbers, Rejection Method, Inversion, rejection, composition and Convolution. Unit-IV Discrete System Simulation: Discrete Events, representation of time, Generation of arrival patterns, Simulation programming tasks, simulation of telephone system, Gathering statistics, delayed calls. Simulation of Queuing Systems: Poisson arrival patterns, Exponential distribution, Normal Distribution Queuing Disciplines, Service times, Simulation of single and two server queue, Application of queuing theory in computer system. Unit-V Introduction to Simulation languages and Analysis of Simulation output GPSS: Classification of simulation languages, Introduction to GPSS Action times, general description, Succession of events, facilities and storage, Choice of paths, Conditional transfers, program control statements, Estimation methods, Relication of Runs, Batch Means , Regenerative techniques, Time Series Analysis, Spectral Analysis and Autoregressive Processes, simulation programming techniques like entry types. References: 1. W.A. Spriet - Computer Oriented Modeling and Simulation. 2. Gorden G., System simulation, Prentice Hall. 3. Seila, Simulation Modeling, Cengage Learning 4. Law .,Simulation Modeling And Analysis, McGraw Hill 5. Deo, System Simulation with Digital Computer, PHI 6. Harrington, Simulation Modeling methods, McGraw Hill 7. Severance, ― System Modeling & Simulation, Willey Pu 8. T.A. Payer - Introduction to simulation 9. B.Barnes - Modelling and Performance Measurement of Computer System. 10. V. Rajaraman ―Analog Simulation‖ PHI

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Name of Program: MCA (BANKING TECHNOLOGY)

60

20

20

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0

L

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CREDITS

Teacher Assessment *

Compulsory

Data Analytics and Business Intelligence

End Sem University Exam

SUBJECT NAME

Teacher Assessment *

MCBT 202

Category

Two Term Exam

SUBJECT CODE

End Sem University Exam

TEACHING & EVALUATION SCHEME THEORY PRACTICAL

4 1 0 5

Legends: L – Lecture; T – Tutorial/Teacher Guided Student Activity; P – Practical; Q/A – Quiz/Assignment/Attendance; MST – Mid Semester Test. *Teacher Assessment shall be based on following components: Quiz/Assignment/Project/Participation in class activities, given that no component shall exceed more than 10 marks. Course Education Objectives (CEOs): The main objective of the course is to provide students a good overview of the ideas, the techniques, recent developments in Analytics in all its forms viz., descriptive, predictive and prescriptive analytics. In the last one decade, analytics has emerged as a catch-all phrase subsuming and connoting various modelling techniques for data-driven analysis such as statistical techniques/models such as multiple linear regression, logistic regression, kmeans clustering, machine learning models including k-nearest neighbour technique, neural networks, decision trees, case-based reasoning, support vector machine, association rule mining, optimization, OLAP etc. Visual analytics, with ample coverage of visualization techniques shall be discussed. Feature selection and dimension reduction techniques shall also be covered. The relationship between analytics and data mining shall be discussed. Also, it aims to formulate datadriven problems as data mining or predictive analytics problems. Numerous case studies from banking, insurance, finance, manufacturing, and bioinformatics shall be discussed. This approach gives the students an ample opportunity to learn the intricate concepts in the most appropriate way and let them develop skills to solve real-life problems using data mining. Further, the ubiquitous presence of unstructured data in many fields shall be discussed with specific reference to text mining and web mining with applications in cyber fraud detection in banking etc. This completes the whole gamut of analytics at the PG level. Concepts of data warehousing and Online Analytical Processing (OLAP), in terms of data models, conceptual design methodologies, metadata and project implementation strategies shall also be discussed. Finally, Big data analytics shall also be introduced. Course Outcomes (COs): By the time students complete the academic requirements for this course, they will be able to:  Solve complex logic problems using the tools and techniques found in Computer Science, Business and Communications.  Analyze data, test claims, and draw valid conclusions using appropriate statistical methodology.  Retrieve, organize and manipulate data using a variety of analytical tools.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications 

Learn visual representation methods and techniques that increase the understanding of complex data and models.

Unit I: Introduction to Analytics Introduction to Analytics; its various forms viz., descriptive, predictive and prescriptive. Introduction to Data Warehousing and its concepts, Data Mining (DM), DM concepts, DM Process; CRISP-DM Methodology, Data Preparation/Pre-processing techniques - Feature Selection methodologies, dimension reduction techniques such as PCA and Transformations. Data Visualization Techniques, Data Balancing Techniques etc. Unit II: Descriptive and Predictive Analytical techniques Association Rule Mining and its Algorithms & Applications; Clustering, Hierarchical and Partition clustering – Techniques and applications; Forecasting- Simple Linear Regression, Multiple Linear Regression; Classification - Logistic Regression, Decision Trees, k-NN, Neural Networks, Case Based Reasoning etc. Unit III: Practical Considerations in Analytics Projects Determination of best analytical/data mining technique, MSE, NRMSE, MAPE, Confusion Matrix, ROC, AUC, Lift, Comprehensibility etc. Unit IV: Applications and Case Studies Analytical CRM applications such as bankruptcy prediction, churn prediction, default prediction, customer segmentation, market basket analysis, credit scoring, Financial Fraud detection; Manufacturing in Hardware industry; Bioinformatics applications for cancer prediction etc. Unit V: Advanced Analytics and Case Studies Unstructured data mining, Text Analytics, Web Mining etc., Cyber Fraud Detection including Phishing/Spam/Malware detection; Overview of prescriptive analytics and application in time series data mining with a case study from banking operations. Introduction to Big Data and applications. Books and References: 1. Data Mining: Practical machine learning tools and techniques by IH Witten, E Frank, Morgan Kaufmann, 2005. 2. Data warehouse lifecycle toolkit: expert methods for designing, developing and deploying data warehouses - Kimball, Ralph; Reeves, Laura et al, John Wiley & Sons, 1998. 3. Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, by Galit Shmueli and Nitin R. Patel, Peter Bruce, 2010, John Wiley. 4. Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications by Gary Miner, John Elder, Andrew Fast, Thomas Hill, Robert Nisbet, Dursun Delen, Andrew Fast, Academic Press, 2012. 5. Data Mining Techniques – A. K. Pujari, University Press, 2001.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications 6. Data mining: concepts and techniques - Han, Jiawei; Kamber, Micheline, J. Pei, Morgan Kaufmann Publishers, 2011. 7. M. N. Murty and V. S. Devi, Pattern Recognition: An Algorithmic Approach, Springer, 2013. 8. C. Bishop, Pattern Recognition and Machine Learning, Springer, 2011 9. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2003 10. ―Introduction to Data Mining‖, Pang-Ning Tan, Pearson Education, 1st edition, 2013.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications

Name of Program: MCA (BANKING TECHNOLOGY) TEACHING & EVALUATION SCHEME

60

20

20

0

0

L T P

CREDITS

Teacher Assessment*

Compulsory

Enterprise Resource Planning (ERP)

End Sem University Exam

SUBJECT NAME

Teacher Assessment*

MCBT 203

Category

Two Term Exam

SUBJECT CODE

PRACTICAL

End Sem University Exam

THEORY

4 1 0 5

Legends: L – Lecture; T – Tutorial/Teacher Guided Student Activity; P – Practical; Q/A – Quiz/Assignment/Attendance; MST – Mid Semester Test. *Teacher Assessment shall be based on following components: Quiz/Assignment/Project/Participation in class activities, given that no component shall exceed more than 10 marks. Course Education Objectives (CEOs):  

 

  

To analyze, design and propose IT solutions for the integration of business process throughout the enterprise Analyze a business’ enterprise activities, workflow and process to identify problems, weaknesses, strengths, threats, opportunities, stakeholders and entities interacting with the enterprise. Propose reengineered enterprise processes that optimize the enterprise’s performance. Design integrated organizational structures and business processes that optimize the enterprise’s performance, overcome problems and weaknesses of current processes, address environmental threats and capitalize on its strengths and opportunities that provide competitive advantage. Propose a plan that applies IT toward integrating the enterprise, aligns various entities to the business’ strategic plan, and addresses adoption issues. Propose enterprise-level IT-based solutions that incorporate new business processes and organizational structures. Propose ERP, SCM, CRM and KM system applications that allow the business to perform efficiently and effectively in competitive markets.

Course Outcomes (COs): A student completing this course will:  Understand and gain insight into process views of organizations and tools and techniques used to model both as-is and to-be models.  Apply the process modeling techniques in one or more modeling environments.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications  

  

 

Know and be able to apply key technical terminology in enterprise information systems as they apply in different ERP products and development methods. Understand key differences between the major ERP applications (such as SAP R/3, and Oracle/PeopleSoft/Sibel) and issues specific to these applications their configuration and management. Analyze a current architecture and perform an effective gap analysis before an ERP implementation. Be able to map enterprise architectural resources to a contemporary Enterprise Architecture mapping tool. Understand and be able to articulate the life cycle stages of any ERP implementation. Effectively describe problems typical of ERP implementation projects and translate this information and use this information to anticipate and articulate the challenges associated with post-implementation management of ERP systems. Synthesize prior theoretical and experiential knowledge in IT development and project management with the current literature on Enterprise System development. Be able to evaluate the progress of an ongoing ERP implementation project.

Unit I Enterprise: An Overview: Business Functions and Business Processes, importance of Information: Characteristics of information; Types of information, Information System: Components of an information system; Different types of information systems; Management information system, Enterprise Resource Planning: Business modeling; Integrated data model. Introduction to ERP: Defining ERP, Origin and Need for an ERP System, Benefits of an ERP System, Reasons for the Growth of ERP Market, Reasons for the Failure of ERP Implementation: Roadmap for successful ERP implementation Unit II ERP and Related Technologies: Business Process Re-engineering, Management Information systems, Decision Support Systems, Executive Information Systems- Advantages of EIS; Disadvantages of EIS, Data Warehousing, Data Mining, On-Line Analytical Processing, Product Life Cycle Management, Supply Chain Management, ERP Security. ERP Implementation Life Cycle: ERP Tools and Software, ERP Selection Methods and Criteria, ERP Selection Process, ERP Vendor Selection, ERP Implementation Lifecycle, Pros and cons of ERP implementation, Factors for the Success of an ERP Implementation. Unit III ERP Modules Structure: Finance, Sales and Distribution, Manufacturing and Production PlanningMaterial and Capacity Planning; Shop Floor Control; Quality Management; JIT/Repetitive Manufacturing; Cost Management ; Engineering Data Management; Engineering Change Control ; Configuration Management ;Serialization / Lot Control ;Tooling, Human Resource, Plant Maintenance- Preventive Maintenance Control; Equipment Tracking; Component Tracking; Plant Maintenance Calibration Tracking; Plant Maintenance Warranty Claims Tracking, Quality Management - Functions of Quality Management; CAQ and CIQ; Materials Management- Pre-purchasing; Purchasing; Vendor Evaluation; Inventory Management and Invoice Verification and Material Inspection.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Unit IV ERP – A Manufacturing Perspective: Role of Enterprise Resource Planning (ERP) in manufacturing, Computer Aided Design/Computer Aided Manufacturing (CAD/CAM), Materials Requirement Planning (MRP)-Master Production Schedule (MPS);Bill of Material (BOM);Inventory Records; Closed Loop MRP; Manufacturing Resource Planning (MRP-II), Manufacturing and Production Planning Module of an ERP System , Distribution Requirements Planning (DRP), Just-in-Time(JIT) & KANBAN - Kanban; Benefits of JIT; Potential Pitfalls of JIT; Kanban, Product Data Management (PDM)- Data Management, Process Management; functions of PDM; Benefits of PDM, Manufacturing Operations- Make-to-Order (MTO) and Make-to-Stock (MTS); Assemble-to-Order (ATO); Engineer-to-Order (ETO); Configure-to-Order (CTO). Unit V ERP: A Purchasing Perspective: Role of ERP in Purchasing, Purchase Module: Features of purchase module; Benefits of purchase module, ERP Purchase System. ERP: Sales and Distribution Perspective: Role of ERP in Sales and Distribution, Sub-Modules of the Sales and Distribution Module: Master data management, Order management, Warehouse management, Shipping and transportation, Billing and sales support, foreign trade, Integration of Sales and Distribution Module with Other Modules. ERP: An Inventory Management Perspective: Role of ERP in Inventory Management: Features of ERP inventory management system; Benefits of ERP inventory management system; Limitations of ERP inventory management system, Importance of Web ERP in Inventory Management, ERP Inventory Management Module, Sub-Modules of the ERP Inventory Management Module, Installation of ERP Inventory Management System, Failure of ERP Inventory Installation. ERP: A CRM Perspective: Role of ERP in CRM, Concept of CRM: Objectives of CRM; Benefits of CRM; Components of CRM, Types of CRM: Operational CRM, Analytical CRM, Sales intelligence CRM, Collaborative CRM, Sub-Modules of CRM: Marketing module; Service module; Sales module. Books and References: 1. Vinod Kumar Garg and N.K.Venkita Krishnan, ―Enterprise Resource Planning- Concepts and Practice‖, PHI, 1998. 2. Ellen Monk and Bret Wagner, ―Concepts in Enterprise Resource Planning‖, Second Edition. 3. Mary Sumner, ―Enterprise Resource Planning‖, Pearson Education, 2007. 4. Daniel E. O'Leary, ―Enterprise Resource Planning Systems: Systems, Life Cycle, Electronic Commerce, and Risk‖. 5. Jose Antonio Fernandez, ―The SAP R/3 Handbook‖, Tata McGraw Hill Publications, 1998. 6. Alexis Leon, ―ERP Demystified‖, Tata McGraw Hill, Second Edition, 2008. 7. Jim Mazzullo,‖SAP R/3 for Everyone‖, Pearson, 2007. 8. Biao Fu, ―SAP BW: A Step-by-Step Guide‖, First Edition, Pearson Education, 2003.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Name of Program: MCA (BANKING TECHNOLOGY) TEACHING & EVALUATION SCHEME

COMPULSORY

Artificial Intelligence

60

20

20

0

Teacher Assessment*

SUBJECT NAME

0

L

T

P

CREDITS

MCBT 204

Category

PRACTICAL Teacher Assessment* End Sem University Exam

SUBJECT CODE

End Sem University Exam Two Term Exam

THEORY

4

1

0

5

Legends: L - Lecture; T - Tutorial/Teacher Guided Student Activity; P – Practical; C - Credit; Q/A – Quiz/Assignment/Attendance, MST - Mid Sem Test. *Teacher Assessment shall be based on following components: Quiz/ Assignment/ Project/ Participation in class (Given that no component shall be exceed 10 Marks) Course Educational Objectives (CEOs):  To study the concepts of Artificial Intelligence.  To understand methods of solving problems using Artificial Intelligence.  To introduce the concepts of Expert Systems and machine learning. Course Outcomes (Cos): Students will have full understanding of the following concepts,  Various Ideas in AI  Various Types of Expert systems UNIT-I Overview of AI Definition of AI, The AI problems, what is an AI technique, Characteristics of AI applications. LISP programming: Syntax and numeric functions, Basic list manipulation functions, predicates and conditionals, General problem solving, various types of production systems. UNIT-II Search and Control Strategies, control strategies forward and backward chaining, exhausive searches depth first breadth first search. Other Search techniques Heuristic Search Techniques Hill climbing, branch and bound technique, best first search & A* algorithm, problem reduction & AO* algorithm, constraint satisfaction problems. UNIT-III Knowledge Representations problems in representing knowledge, First order predicate calculus, skolemization, resolution principle & unification, interface mechanisms, horn's clauses, semantic networks, frame systems and value inheritance, conceptual dependency ,scripts.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications UNIT-IV Planning study block word in robotics, component of planning systems, goal stack planning, and non linear planning. Natural Language processing Parsing techniques, context free grammar, case and logic grammars, semantic analysis. Game playing like Minimax search procedure, alpha-beta cutoffs. UNIT-V Introduction to fuzzy logic, neuro fuzzy, and soft computing, from conventional AI to computational intelligence Probability theory, bayes theorem, certainty factor. Expert Systems Introduction to expert system and application of expert systems, various expert system shells, knowledge acquisition, case studies, MYCIN. Learning Rote learning, learning by induction, explanation based learning. References: 1. Charniak and Mcdermott. Introduction to Artificial Intelligence , Addison-Wesley, 1985. 2. Dan W. Patterson ―Introduction to Artifical Intelligence and Expert Systems‖, Prentice India 3. Winston. Artificial Intelligence , 3rd 4. Elaine Rich and Kevin Knight ―Artifical Intelligence‖ - Tata McGraw Hill. 5. ―Artifical Intelligence‖ 4 ed. Pearson. 6. Nils J. Nilson ―Principles of Artifical Intelligence‖, Narosa Publishing House. 7. Clocksin & C.S.Melish ―Programming in PROLOG‖, Narosa Publishing House. 8. M.Sasikumar,S.Ramani etc. ―Rule based Expert System‖, Narosa Publishing House.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Name of Program: MCA (BANKING TECHNOLOGY) TEACHING & EVALUATION SCHEME

Soft Computing

60

20

20

0

0

L

T

P

CREDITS

Elective - A

Teacher Assessment

MCBT 205 E2(A)

End Sem University Exam

SUBJECT NAME

Teacher Assessment

Category

Two Term Exam

SUBJECT CODE

PRACTICAL

End Sem University Exam

THEORY

4

1

0

5

Legends: L – Lecture; T – Tutorial/Teacher Guided Student Activity; P – Practical; Q/A – Quiz/Assignment/Attendance; MST – Mid Semester Test. *Teacher Assessment shall be based on following components: Quiz/Assignment/Project/Participation in class activities, given that no component shall exceed more than 10 marks. Course Education Objectives (CEOs): Soft Computing methodologies handle imprecision, uncertainty, complexity and partial truth of information arising in real life systems, which include fuzzy logic, rough set, neural networks, and evolutionary computation (EC) as core methodologies. Soft computing methods have proved to be very useful for machine intelligence, automation and technology based Applications demanding high Computational Intelligence. This course covers fundamentals of some important methodologies of Soft computing. It also focuses on their application to Engineering, Economics, Finance and Banking Management. The course deals with Matlab and its relevant toolboxes such as Optimization toolbox, fuzzy logic toolbox, neural network toolbox and control system toolbox along with relevant problems and case studies. Course Outcomes (COs): Students acquire knowledge of soft computing theories fundamentals and so they will be able to design program systems using approaches of these theories for solving various real-world problems. Students awake the importance of tolerance of imprecision and uncertainty for design of robust and low-cost intelligent machines. Unit I: Fuzzy Sets and Fuzzy Logic: Introduction, fuzzy sets versus crisp sets, fuzzy relations, extension principles, fuzzy numbers, linguistic variable, hedges, fuzzy logic, fuzzy rule base design and analysis, fuzzy control system, fuzzy segmentation and clustering, fuzzy decision making. Unit II: Artificial Neural Networks Basic models, single and multi layer perceptions, back propagation algorithm for MLP, support vector machine, radial basis function neural networks, general regression neural networks, Probabilistic neural networks, Kohonen’s self-organizing feature map, deep learning and deep neural network.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Unit III: Evolutionary Techniques: Basics of genetic algorithm (GA), schema theorem and convergence of GA, differential evolution, ant colony optimization, particle swarm optimization. Unit IV: Rough Sets Definition, upper and lower approximations, boundary region, definability, roughness, reduct and core, decision matrices and applications. Unit V: Hybrid Systems: Neural network based fuzzy Systems, fuzzy logic based neural networks, genetic Algorithm for neural network design and learning, fuzzy logic and genetic algorithm for optimization. Books and References: 1. T. J. Ross, ―Fuzzy logic with engineering applications‖, 3 rd Edition, John Wiley & Sons, (2010). 2. H.-J. Zimmermann, ―Fuzzy set theory and its applications‖, 4 th edition, Kluwer Academic Publishers, (2001). 3. G. Bojadziev and M. Bojadziev, ―Fuzzy sets, fuzzy logic, applications‖, World Scientific, (1995). 4. G. J. Klir and B. Yuan, ―Fuzzy sets and fuzzy logic: theory and applications‖ Prentice Hall, (1995). 5. S. Haykin, ―Neural networks and learning machines‖ 3 rd edition, Prentice Hall, (2008). 6. D. W. Patterson, ―Artificial neural networks: theory and applications‖, Prentice Hall, (1998). 7. M. H. Hassoun, ―Fundamentals of artificial neural network‖, MIT Press, (1995). 8. D. E. Goldberg, ―Genetic algorithms in search and optimization, and machine learning‖, Addison-Wesley, (1989) 9. K. Deb, ―Multi-objective optimization using evolutionary algorithms‖, Wiley India Pvt Ltd, (2010). 10. C.-T. Lin and C. S. G. Lee, ―Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems‖, Prentice Hall, (1996). 11. Z. Pawlak, ―Rough sets: theoretical aspects of reasoning about data‖, Kluwer Academic Publisher, (1991).

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Name of Program: MCA (BANKING TECHNOLOGY) TEACHING & EVALUATION SCHEME

60

20

20

0

0

L

T

P

CREDITS

Teacher Assessment*

Financial Market And International Banking

End Sem University Exam

Elective - B

SUBJECT NAME

Teacher Assessment*

MCBT 205 E2(B)

Category

Two Term Exam

SUBJECT CODE

PRACTICAL

End Sem University Exam

THEORY

4

1

0

5

Legends: L - Lecture; T - Tutorial/Teacher Guided Student Activity; P – Practical; C - Credit; Q/A – Quiz/Assignment/Attendance, MST - Mid Sem Test. *Teacher Assessment shall be based on following components: Quiz/ Assignment/ Project/ Participation in class (Given that no component shall be exceed 10 Marks) Course Educational Objectives (CEOs):  

Familiarizing the students with the Indian capital market, its operations, instruments, regulations etc. To enable the students learn nature, scope and structure of International Business, and understand the influence of various environmental factors on international business operations.

Course Outcomes (COs): Student will be able to:  Developing an appreciation among the students for the interfaces among government policies, capital market, investors and firms. UNIT I: Introduction to Financial Markets 1. Introduction of Financial Markets 2. Structure of Financial System 3. Role of Financial System in Economic Development 4. Financial Markets and Financial Instruments 5. Government Economic Philosophy 6. Structure of Financial Market in India

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications UNIT II: Primary Market System and Regulations in India 1. Types of firm’s Interface with Investors 2. Types of Scripts 3. Issue of Capital: Process, Regulations, Legalities 4. Pricing of Issue, Methods of Issue 5. Book-building 6. Intermediaries in Issue Management 7. Managing Shareholder Relations UNIT III: Secondary Market System and Regulations in India 1. Stock Exchanges in India: History and development and importance 2. Listing of Scripts, On-line Trading 3. Depositories: Growth, Development, Regulation, Mechanism 4. OTC Exchange 5. Stock Exchange Mechanism: Trading, Settlement, Risk Management 6. Inside Trading, Circular Trading 7. Players on Stock Exchange: Investors, Speculators, Market Makers, Bulls, Bears, Stags 8. Role of FIIs, MFs and Investment Bankers UNIT IV: Introduction to International Business 1. Importance, nature and scope of International business 2. Modes of entry into International Business 3. Internationalization process and managerial implications 4. Multinational Corporations and their involvement in International Business 5. Foreign investments 6. Technology transfer, pricing and regulations 7. International collaborative arrangements and strategic alliances. UNIT V: International Business Environment 1. Economic, Political, Cultural and Legal environments in International Business. 2. Framework for analyzing international business environment. Concept and significance of balance of payments account 3. WTO, IMF, World Bank References: Bennet, Roger, International Business, Financial Times, Pitman Publishing, London. Bhattacharya, B., Going International: Response Strategies of the Indian Sector, Wheeler Publishing, New Delhi. 3. Czinkota, Michael R., et. al., International Business, the Dryden Press, Fortworth. Department of Commerce, University of Delhi 4. Danoes, John D. and Radebaugh, Lee H., International Business: Environment and Operations, Addison Wesley, Readings. 5. Hill, Charles W. L., International Business, McGraw Hill, New York. 1. 2.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Name of Program: MCA (BANKING TECHNOLOGY) TEACHING & EVALUATION SCHEME

60

20

20

0

0

L

T

P

CREDITS

Teacher Assessment*

Elective-C Grid Computing

End Sem University Exam

SUBJECT NAME

Teacher Assessment*

MCBT 205 E2(C)

Category

Two Term Exam

SUBJECT CODE

PRACTICAL

End Sem University Exam

THEORY

4

1

0

5

Legends: L - Lecture; T - Tutorial/Teacher Guided Student Activity; P – Practical; C - Credit; Q/A – Quiz/Assignment/Attendance, MST - Mid Sem Test. *Teacher Assessment shall be based on following components: Quiz/ Assignment/ Project/ Participation in class (Given that no component shall be exceed 10 Marks) Course Educational Objectives (CEOs):      

To Understand and explain the key concepts of Grid computing. To Identify the resource selection for Grid environment. To understand about Grid computing history ,evolution of Grid and its Security issues To understand Data management and transfer in Grid environments. To know about Resource management technologies for Grid. To understand the recent versions of Globus toolkit.

Course Outcomes (COs):       

Students will understand the key concepts of Grid computing. Students will identify the resource selection for Grid environment. Students will understand about Grid computing history, evolution of Grid and its Security issues. Students will know about Data management and transfer in Grid environments Students will understand Resource management technologies for Grid. Students will understand the recent versions of Globus toolkit The students will be encouraged to adapt their research problem in a Grid environment as a project.

UNIT-I Introduction: Parallel and Distributed Computing, Cluster Computing, Grid Computing Early and Current Grid Activities, Grid Computing Organizations and Their Roles: Developing Grid Standards & Best Practice Guidelines, Developing Grid Computing Toolkits & Frameworks, to

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Solve Computing using Grid-Based Solutions, Data, and Network Requirements, Building and Using Grid-Based Solutions Commercially, Grid Business Areas, Grid Applications. UNIT-II Grid Monitoring Architecture (GMA): An Overview of Grid Monitoring Systems. Grid Computing Anatomy: The Grid Problem, The Grid Computing Roadmap, Web Services and Grid Services Architecture. UNIT-III OGSA: Introduction, Platform Components of OGSA, Open Grid Services Infrastructure (OGSI), Basic Services of OGSA. Grid Security: A Brief Security Primer-PKI-X509 Certificates. UNIT-IV Grid Development Toolkits: Globus GT3 Toolkit: Architecture, Programming Model, Implementation and High-Level Services, Data Management-Categories and Origins of Structured Data-Data Management Challenges. UNIT-V Case Studies- Recent version of Globus Toolkitand gLite-Architecture, Components and Features. Message Passing Interface (MPI) Standard: Overview, Arguments and Procedures, Data Types, Processes, Error Handling, Platform independence, Point-to-Point and Collective Communication, Groups- Contexts Communicators, Process Technologies. References: Joshy Joseph & Craig Fellenstein, ―Grid Computing‖, Pearson Education 2004. Joshy Joseph, Craig Fellenstein—Grid Computing, Pearson Education, 2004. Vladimir Silva,Grid Computing for Developers, Dreamtech Press, 2006. Ian Foster & Carl Kesselman, The Grid 2 – Blueprint for a New Computing Infrascture , Morgan Kaufman, 2004 5. Fran Berman,Geoffrey Fox, Anthony J.G.Hey, ―Grid Computing: Making the Global Infrastructure a reality‖, John Wiley and sons, 2003. 6. Ahmar Abbas--Grid Computing —A Practical Guide to Technology and Applications, Firewall Media, 2006. 1. 2. 3. 4.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications Name of Program: MCA (BANKING TECHNOLOGY) TEACHING & EVALUATION SCHEME

COMPULSORY

SUBJECT NAME

Lab 1 (Minor Project based on ERP)

0

0

0

90

60

L

T

P

CREDITS

MCBT 206

Category

Teacher Assessment*

SUBJECT CODE

PRACTICAL

End Sem University Exam Two Term Exam Teacher Assessment* End Sem University Exam

THEORY

0

0

4

2

Legends: L - Lecture; T - Tutorial/Teacher Guided Student Activity; P – Practical; C - Credit; Q/A – Quiz/Assignment/Attendance, MST - Mid Sem Test. *Teacher Assessment shall be based on following components: Quiz/Assignment/Project/Participation in class (Given that no component shall be exceed 10 Marks) Course Education Objectives (CEOs):  

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To analyze, design and propose IT solutions for the integration of business process throughout the enterprise Analyze a business’ enterprise activities, workflow and process to identify problems, weaknesses, strengths, threats, opportunities, stakeholders and entities interacting with the enterprise. Propose reengineered enterprise processes that optimize the enterprise’s performance. Design integrated organizational structures and business processes that optimize the enterprise’s performance, overcome problems and weaknesses of current processes, address environmental threats and capitalize on its strengths and opportunities that provide competitive advantage. Propose a plan that applies IT toward integrating the enterprise, aligns various entities to the business’ strategic plan, and addresses adoption issues. Propose enterprise-level IT-based solutions that incorporate new business processes and organizational structures. Propose ERP, SCM, CRM and KM system applications that allow the business to perform efficiently and effectively in competitive markets.

Course Outcomes (COs): A student completing this course will:  Understand and gain insight into process views of organizations and tools and techniques used to model both as-is and to-be models.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications   

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Apply the process modeling techniques in one or more modeling environments. Know and be able to apply key technical terminology in enterprise information systems as they apply in different ERP products and development methods. Understand key differences between the major ERP applications (such as SAP R/3, and Oracle/PeopleSoft/Sibel) and issues specific to these applications their configuration and management. Analyze a current architecture and perform an effective gap analysis before an ERP implementation. Be able to map enterprise architectural resources to a contemporary Enterprise Architecture mapping tool. Understand and be able to articulate the life cycle stages of any ERP implementation. Effectively describe problems typical of ERP implementation projects and translate this information and use this information to anticipate and articulate the challenges associated with post-implementation management of ERP systems. Synthesize prior theoretical and experiential knowledge in IT development and project management with the current literature on Enterprise System development. Be able to evaluate the progress of an ongoing ERP implementation project.

Books and References: 1. Vinod Kumar Garg and N.K.Venkita Krishnan, ―Enterprise Resource Planning- Concepts and Practice‖, PHI, 1998. 2. Ellen Monk and Bret Wagner, ―Concepts in Enterprise Resource Planning‖, Second Edition. 3. Mary Sumner, ―Enterprise Resource Planning‖, Pearson Education, 2007. 4. Daniel E. O'Leary, ―Enterprise Resource Planning Systems: Systems, Life Cycle, Electronic Commerce, and Risk‖. 5. Jose Antonio Fernandez, ―The SAP R/3 Handbook‖, Tata McGraw Hill Publications, 1998. 6. Alexis Leon, ―ERP Demystified‖, Tata McGraw Hill, Second Edition, 2008. 7. Jim Mazzullo,‖SAP R/3 for Everyone‖, Pearson, 2007. 8. Biao Fu, ―SAP BW: A Step-by-Step Guide‖, First Edition, Pearson Education, 2003.

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications

Name of Program: MCA (BANKING TECHNOLOGY) TEACHING & EVALUATION SCHEME

COMPULSORY

Lab-2 (Modeling and Simulation Lab)

0

0

0

60

Teacher Assessment*

SUBJECT NAME

40

L

T

P

CREDITS

MCBT 207

Category

PRACTICAL Teacher Assessment* End Sem University Exam

SUBJECT CODE

End Sem University Exam Two Term Exam

THEORY

0

0

4

2

Legends: L - Lecture; T - Tutorial/Teacher Guided Student Activity; P – Practical; C - Credit; Q/A – Quiz/Assignment/Attendance, MST - Mid Sem Test. *Teacher Assessment shall be based on following components: Quiz/Assignment/Project/Participation in class (Given that no component shall be exceed 10 Marks) Course Educational Objectives (CEOs):  To develop mathematical models of phenomena involved in various chemical engineering processes and solutions for these models.  To introduce students to basic simulation methods and tools for modelling and simulation of continuous, discrete and combined systems. Course Outcomes (COs):  Understand the important physical phenomena from the problem statement  Develop model equations for the given system  Demonstrate the model solving ability for various processes/unit operations  Demonstrate the ability to use a process simulation List of Experiments: 1. Simulate CPU scheduling algorithm using queuing system a) FCFS b) SJF c) Priority Algo 2. Simulate multiplexer/concentrator using queuing system. 3. Simulate congestion control algorithms. 4. Simulate disk scheduling algorithms. 5. Simulate a Manufacturing shop and write a program in GPSS. 6. Simulate Telephone system model and write a program in SIMSCRIPT. 7. Simulation of continuous system. 8. Simulation of the R-C amplifier circuit. 9. Generation of Random number. 10. Simulation mass spring damper system

Shri Vaishnav Vidhyapeeth Vishvavidhyalaya, Indore Institute of Computer Applications 11. . Simulation of National econometric system.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

W.A. Spriet - Computer Oriented Modeling and Simulation. Gorden G., System simulation, Prentice Hall. Seila, Simulation Modeling, Cengage Learning Law .,Simulation Modeling And Analysis, McGraw Hill Deo, System Simulation with Digital Computer, PHI Harrington, Simulation Modeling methods, McGraw Hill Severance, ― System Modeling & Simulation, Willey Pu T.A. Payer - Introduction to simulation B.Barnes - Modelling and Performance Measurement of Computer System. V. Rajaraman ―Analog Simulation‖ PHI