M. Tech. Technology Management

DEPARTMENT OF FUTURES STUDIES

SCHOOL OF TECHNOLOGY, UNIVERSITY OF KERALA

Outcome Based Curriculum

M. Tech. Technology Management Syllabus effective from 2020 Admission onwards

Programme Outcomes (POs)

PO1 Students become capable to carry out research and development work independently

PO2 Achieve the ability to prepare and present a substantial technical report

PO3 Improve skills in solving scientific problems and designing devices.

PO4 Students will become able to demonstrate a degree of mastery over the area as per

the specialization of the program.

Programme Specific Outcomes (PSOs)

PSO1 Understand principles and concepts of technology management

PSO2 Develop necessary skills for technology forecasting and assessment for technology

management

PSO3 Improve skills in solving scientific problems of interdisciplinary nature in academia

and industry

PSO4 Students will become able to demonstrate a degree of mastery over the area as per

the specialization of the program.

PSO5 Develop necessary mathematical and computational skill related to technology man-

agement and allied areas

PSO6 Develop problem solving skill of interdisciplinary nature of present and futuristic

problems

PSO7 Develop skills to act as technology managers in industry

Structure of the Programme

Sem No. Course Code Name of the Course Number of

Credits

I

Core Courses

FUS-CC-611

Foresight and Futures Research

2

FUS-CC-612 Technology Forecasting and Assessment 3

FUS-CC-613 Management of Innovation 3

FUS-CC-614 Systems Analysis and Design 3

FUS-CC-615 Operations Research and Decision Theory 3

FUS-CC-616 Lab I: Python for Data Analysis 3

Total Credits for Semester I 17

II

Core Courses

FUS-CC-621

Combinatorial Optimization

3

FUS-CC-622 Modelling and Simulation 3

FUS-CC-623 Principles of Technology Management 3

FUS-CC-624 IPR and Patents: Law, Practice and Consultancy 2

FUS-CC-625 Lab II: Statistical Computing with R 3

Internal Electives

Elective 1

Elective 2

FUS-DE-626 Artificial Intelligence for Technology Leaders 3

FUS-DE-627 Econometrics and Economic Forecasting 3

FUS-DE-628 Total Quality Management 3

FUS-DE-629 Computer Aided Design 3

FUS-DE-6210 Environmental Engineering 3

FUS-DE-6211 Energy, Ecology & Environment 3

FUS-DE-6212 Strategic Management 3

FUS-DE-6213 Knowledge Management 3

FUS-DE-6214 Computer Aided Decision Support Systems 3

FUS-DE-6215 Intelligent Manufacturing Systems 3

Total Credits for Semester II

20

III

Core Courses

FUS-CC-631

Research Methodology and Negotiated Studies

3

FUS-CC-632 Industrial Project 4

FUS-CC-633 Dissertation (Stage I) 6

Internal Electives Elective 3

Elective 4

FUS-DE-634 System Dynamics Simulation 3

FUS-DE-635 Intelligent and Knowledge Based systems 3

FUS-DE-636 Planning and Management of Human Resources 3

FUS-DE-637 Structure and Analysis of Complex Networks 3

FUS-DE-638 Management Information Systems 3

FUS-DE-639 Software Projects Management 3

Total Credits for Semester III 19

IV

FUS-CC-641 Dissertation (Stage II) 16

Extra Departmental Elective Courses

I FDS-GC-411 Foresight and Futures Research 2

III FDS-GC-431 Parallel Programming with MPI 2

Any FUS-GC-A1 Scientific Research Paper Writing 2

Codes: CC = Core; DE = Discipline-Specific Elective; GC =Generic course.

Semester: I Course Code: FUS-CC-611 Credits: 2

FORESIGHT AND FUTURES RESEARCH

Learning Outcomes : On completion of the course the student will be able to

CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the critical concepts in futures studies PO4/ PSO2, 4, 5 U F, C

CO2 Understand the six Basic Concepts and six Basic

Pillars of Futures Studies

PSO2,5,6 U F,C,P

CO3 Apply methods of foresight and futures research PSO2, 6 AP C, P

CO4 Evaluate various aspects of scenario building PSO4,6 E C, P

CO5 Evaluate the implications of scenario building and

Opportunity Analysis

PSO2,4,6 E C, P

CO6

Understand the Interpretive Structural Modelling

and evaluate its applications to objective setting

and policy making

PO4 / PSO2,4,6 E C, P

(CL- Cognitive Level: R-remember, U-understand, AP- Apply, AN- analyses, E- evaluate, CR- create,

KC- Knowledge Category: F-Factual, C- Conceptual, P-Procedural, M- Metacognitive)

Assessment Pattern (Internal & External)

Bloom’s Category Continuous Assessment Tests (percentage) Terminal Examination

(percentage) 1 2 3

Remember 10 10 10 10

Understand 30 20 20 20

Apply 40 40 40 40

Analyse 10 10 10 10

Evaluate 10 10 10 10

Create

COURSE CONTENT

MODULE I

Review futures basics- Become acquainted with key concepts, terms, and perspectives of the futures field.- In-

troduce some of the publications and organizations in the field and discuss futures skills- Identify the major

events and founders in the history of future studies - Discuss what it means to be a futurist- Role of a futurist

MODULE II

Six Basic Concepts and six Basic Pillars of Futures Studies- Prominent Futures Schools

MODULE III

Environmental Monitoring, Scanning-Identify and monitor key quantities and conditions that indicate the base-

line occurring -Environmental scanning as the means to keep current on change within a specific domain- Prac-

tice environmental scanning and assess how well each scanning hit fits the ideal criteria -Use scanning hits as

the empirical support for alternative plausible futures

MODULE IV

Creativity- Scenarios -The key concepts, terms, and approaches to creativity- A standard approach to creative

problem solving and a variety of other Approaches - Scenario Theory- Key concepts, terms, and criteria of good

scenarios-The use of scenarios in professional practice -A review of different ways that people can build scenar-

ios Strengths and weaknesses of various scenario approaches

MODULE V

Implications and Opportunity Analysis - Drawing out the implications of scenarios using, brainstorming, fu-

tures wheels, expert panels and judgment- Identify challenges of specific scenarios for specific domains- Use

those challenges to prioritize strategic issues that should be addressed- See change as resulting from trends,

events, issues and images- Pick up the weak signals of coming change through environmental scanning - Set-

ting up a good scanning system- Progress/Decline: That social change is generally an improvement of the hu-

man condition; or alternatively, that societies have descended from a better period to today. Development: That

social change moves in a definite direction, but that the direction is neutral—not necessarily any better or any

worse than the past, just different.

MODULE VI

Structural modelling-Interpretive Structural Modelling (ISM)- Reachability matrix models-From mental models

to ISM-Applications to Objective setting, Planning and Policy making for Futuristic socie-

tal/governmental problems.

REFERENCES

1. Bill Ralston & Ian Wilson, The Scenario Planning Handbook

2. Jerry Glenn & Ted Gordon (eds.), Futures Research Methodology V3.0, CD, Washington DC, Millen-

nium Project, 2012.

3. Pero Micic, The Five Futures Glasses, 2010

4. Thomas Chermack, Scenario Planning in Organizations: How to Create, Use, and Assess Scenarios,

2011

5. Trevor Noble (2000), Social Theory and Social Change, New York: St Martin’s Press.

6. Bell, Wendell. Foundations of futures studies, volume 1: Human science for a new era. Vol. 1. Transac-

tion publishers, 2004.

7. Bell, Wendell. Foundations of Futures Studies: Volume 2: Values, Objectivity, and the Good Society.

Routledge, 2017.

8. Sage, Andrew P. "Methodology for large-scale systems." (1977).

ADDITIONAL REFERENCES

1. https://en.wikiversity.org/wiki/Introduction_to_Futures_Studies

2. http://www.csudh.edu/global_options/IntroFS.HTML

3. https://cals.arizona.edu/futures/


TECHNOLOGY FORECASTING AND ASSESSMENT

Learning Outcomes: On completion of the course the student will be able to

CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the reasons for technology forecasting PO4/ PSO1, 2 U F, C

CO2 Understand the methods of technology forecasting PSO2,5,6 U F,C,P

CO3 Carry out delphi procedure in given situation for

technology forecasting

PSO2, 6 AP C, P

CO4 Apply growth curve methods of technology fore-

casting techniques for given set of data

PSO4,6 AP C, P

CO5 Carry out technology assessment PSO2,4,6 E C, P

CO6 Understand the fundamentals of system dynamics,

cross-impact analysis. Simulation models

PO4 / PSO2,4,6 U C, P

CO7 Compare various technology forecasting methods PO4/ PSO2, 6 E C,P



Bloom’s Cate-

gory

Continuous Assessment Tests (percentage) Terminal Examination

(percentage)

1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I Introduction to technology forecasting: What Is Technology Forecasting? - Models of Technology Growth and

Diffusion - Technology Forecasting in Context - What Makes a Forecast Good? - Common Errors in Forecasting

Technology - Why Forecast Technology? Methods of Forecasting

MODULE II

Delphi Introduction - Advantages of Committees - Disadvantages of Committees- The Delphi Procédure- Con-

ducting a Delphi Sequence- Variations on Delphi- Guidelines for Conducting a Delphi Sequence- Selecting Del-

phi Panel Members- Constructing Delphi Event Statements -Forecastingby Analogy: introduction - Problems

with Analogies- Dimensionsof Analogies - Selecting Analogous Technologies - Measures of Technology: Scor-

ing Models - Constrained Scoring Models

MODULE III

Growth Curves: The Pearl Curve- The Gompertz Curve- Comparison of the Pearl end Gompertz Curves- linear

– Exponential – Fisher-Pry – Bas Lead-Lag Correlation models - TrendExtrapolation: Introduction- Exponential

Trends- Examples- Model for Exponential Growth

MODULE IV

Features of technology assessment - Objectives of technology assessment - Distinction between technology as-

sessment and environment impact analysis - Types of technology assessment / environment impact analysis -

technology assessment as a map for alternate futures - components of technology assessment - the technology

delivery systems - social impact analysis - Limitations of technology forecasts and assessment

MODULE V

Simulation: Monte Carlo simulation – system dynamics - cross-impact analysis

MODULE VI

Case Studies on Technology Forecasting

REFERENCES

1. Alan Thomas Roper, Scott W. Cunningham, Alan L. Porter, Thomas W. Mason, Frederick A. Rossini,

Jerry Banks, ForecastingAnd ManagementOf Technology, John Wiley & Sons, Inc., 2011

2. Ernest Braun: Technology in Context: Technology Assessment for Managers (Management of Technol-

ogy and Innovation), Routledge 3. Martino, J.P., Technological Forecasting for Decision Making (third edition), MeGraw-Hill, 1993.

4. Porter, A.L., A.T. Roper, T. Mason, R. Rossini, T. Roper, J. Banks, Forecasting and Management of

Technology, John Wiley & Sons, NY, 1991.

5. Sherry R. Arnstein, Alexander N. Christakis Perspectives on technology assessment-, Science and

Technology Publishers 6. Sullivan, W.G. and W.W. Claycombe, Fundamentals of Forecasting, Prentice-Hall, Reston, VA, 1977.

7. Vanston, J.H., Technology Forecasting: An Aid to Effective Technology Management, Technology Fu-

tures Inc., Austin, TX, 1998.


MANAGEMENT OF INNOVATION


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the importance of innovation and tech-

nology as the key drivers of competitive advantage

in today's knowledge economy.

PSO4/

PSO1,4,7

U F,C

CO2 Understand various theories of innovation PSO1,2,4 U F,C

CO3 Apply innovation as a management process PSO1,2,4,6,7 AP C,P

CO4 Evaluate the situations in industry for potential ap-

plication of innovation for promotion business

PSO1,3,6,7 E C,P

CO5 Evaluate the role of financing entrepreneurial activ-

ity

PO4 /

PSO1,7

E C. P

CO6 Evaluate the role globalization on innovation PSO1,7 E C





(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Innovation Management - Invention and Innovation - Technological innovations and creativity – Technological

innovations and Strategy – Developing a firm’s innovative capabilities – Enactment of Technology Strategy -

Technology and innovation management

MODULE II

Schumpeterian Theory of Innovation – Modern Models of innovation: Static and Dynamic Models – Incre-

mental – Radical dichotomy, Abernathy –Clark model, Henderson-Clark Model, Innovation value-added chain ,

Strategic Leadership View, Familiarity matrix, The Teece model, Utterback and Abernathy model, Tushman-

Rosenkopf Technology Life Cycle Model, S- Curve

MODULE III

The business potential of an innovation - Innovation as a management process - Reducing uncertainty associated

with innovation: Technological, Market, Business Uncertainties - the underpinnings of profits: competencies,

endowments and knowledge – shortcomings of S curve- Sibling S Curves.

MODULE IV

Financing entrepreneurial activity – Debt, Equity, Venture Capital, Angel Investors – Actors in the field – NPV –

Options - Role of government in innovation – Educator, Godfather, Babysitter

MODULE V

Globalisation and innovations – Transnational, Global, International, Multidomestic – Strategic choices - Strate-

gic innovation process.

MODULE VI

Case Studies on various aspects of innovation

REFERENCES

1. Allan Afua (2002) Innovation Management: Strategies Implementation & Profits. Oxford University

Press

2. For Case Studies: (https://mitsloan.mit.edu/LearningEdge/Pages/Case-Studies.aspx)

3. Paul Trott, Innovation management and new product development: 5th Edition Prentice Hall

4. Robert A. Burgelman, Clayton M. Christensen, Steven C. And Wheelwright (2009) Strategic Man-

agement of Technology and Innovation (5th edition) McGraw-Hill Irwin, Boston. 5. Scott Shane (2008) The Handbook of Technology and Innovation Management. Wiley-Blackwell


SYSTEMS ANALYSIS AND DESIGN


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the concepts of Systems Analysis and

Design

PO4/PSO6,7 U F,C

CO2 Apply principles of system development life cycle

and project management

PO4/PSO6,7 U F,C

CO3 Understand system requirements elicitation meth-

ods

PO3,4/PSO6,7 AP C,P

CO4 Understand and apply process modeling and logic

modelling

PO3,4/PSO4,6,7 E C,P

CO5 Evaluate and create entity relationship models PO3,4/PSO4,6,7 E C. P

CO6

Understand User Interface Design, Conversion,

Training strategies and various Quality Manage-

ment and Process models

PO3,4/PSO4,6,7 E C





(percentage)

1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Systems Overview, Characteristics and types of system, Manual and automated systems, Information Systems-

Types of Information Systems, Need for Systems analysis and Design, Roles of systems analyst.

MODULE II

Introduction to Systems Development Life Cycle (SDLC), Various phases of development: Analysis, Design,

Development, Testing, Implementation and Maintenance. Lifecycle models: Waterfall model, V model, Spiral

model, Iterative model, Big Bang model, Introduction to Agile methodology and Rational Unified Process

(RUP), Project initiation, Feasibility study – Economic, Technical and Operational Feasibilities, Project Sched-

uling – Gantt Charts, CPM, PERT.

MODULE III

Introduction to UML. Requirements Elicitation Methods: Interviewing, Using Questionnaires, Observation,

Document Analysis, Shadowing and JAD. Use case modeling.

MODULE IV

Process Modeling: Activity Diagrams, Data Flow Diagrams (DFD), DFD conventions, Leveling of DFDs, Lev-

eling Rules, Logical and Physical DFDs.

Logic Modeling: Introduction to Software Architecture and Design phases. Procedure Specifications in Struc-

tured English, Decision Tables and Decision Trees for Complex Logical Specifications.

MODULE V

Data Modeling: Entity relationship model, E-R diagrams, Relationships cardinality, The Three Steps of Nor-

malization, Relational Database

MODULE VI

User Interface Design, Types of User Interface, Conversion to a new System- Conversion strategies, Training

Users- Training Strategies, Guidelines for training, Security Concerns for systems, Case Studies. Introduction to

Quality Management and Process models: ISO, CMM, CMMI, PCMM.

REFERENCES

1. Dennis, Alan, Barbara Wixom, and Roberta Roth. Systems Analysis and Design-2012, John Wiley &

Sons, Inc., 2012. 2. Kendall, K., & Kendall, J., Systems analysis and design (9th ed.). Prentice Hall, 2014.

3. Satzinger, J. W. R. B. Jackson and S. D. Burd. Systems Analysis and Design in a Changing

World, 6th ed. Boston, USA: Thomson Course Technology, 2012.

4. Whitten, Jeffrey L., and Lonnie D. Bentley. Introduction to systems analysis and design. McGraw

Hill Irwin, 2008.


OPERATIONS RESEARCH AND DECISION THEORY


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the concepts, methods and procedure of op-

erations research and decision theory

PSO5,7 C P

CO2 Recommend the procedure of operations research to

formulate and solve linear problems

PO3,4 /

PSO3,4,5,7

C P

PCO3 Apply the concepts and methods of decision theory PSO2,5,7 C P

CO4

Apply the concepts and methods of Non-Linear pro-

gramming, Convex programming and Goal Program-

ming

PO3,4 /

PSO2,5,7

C P

CO5 Understand queening models PSO5,7 C P

CO6 Understand inventory models PO3,4 /

PSO5,7

C P




Bloom’s Cate-

gory


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Introduction to OR - History, nature, scope and phases - OR and decision theory, types of models in Operations

Research: Linear, Integer Linear, Mixed Integer-Linear, Non-Linear etc.

MODULE II

Formulating linear programs: Graphical Solution, The simplex algorithm: Basic feasible solutions and standard

form, Certificates of optimality, infeasibility and unboundedness, Duality and sensitivity analysis, The dual

problem, Duality theorems, Complementary slackness, Changing the right hand side, Changing the objective

function

MODULE III

Structure of decision strategies - Decision trees -Decision under competitive situation - Theory of games - pure

and mixed strategies

MODULE IV

Introduction to Non-Linear programming and Convex programming, Goal Programming

MODULE V

Queuing/Waiting Line Models (basic models)

MODULE VI Inventory Models (basic models)

REFERENCES

1. Billy E. Gillett, Introduction to operations research: a computer-oriented algorithmic approach, Tata

McGraw-Hill 2. George Bernard Dantzig, Mukund Narain Thapa Linear programming: theory and extensions 1& 2 , ,

Springer

3. Hamdy Taha Operations Research, , PHI

4. Woolsey L.A and G .L Nemhauser Integer and Combinatorial Optimization –– John Wiley


1. http://www.personal.psu.edu/cxg286/Math484_V1.pdf

2. http://www.pondiuni.edu.in/storage/dde/downloads/mbaii_qt.pdf


LAB I: PYTHONFORDATAANALYTICS


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand Python for Data Science and Machine Learning PO4/

PSO2,3

U C

CO2 Apply Artificial Neural Networks (ANNs), Convelutional

Neural Networks (CNNs) with TensorFlow 2.0 and Deep learning

PSO5 AP P

CO3 Demonstrate how to Pre-Process the Data PSO5 AP P

CO4 Carryout various regressions in Python PSO3,5,6 AP P

CO5 Recommend different classification Algorithms in Python PSO5,6 E P

CO6 Carryout K means and Hierarchical Cluster Analysis PSO5,6 AP P

CO7 Carryout data analysis with NumPy and Pandas PSO5,6 AP P

CO8 Carryout data visualization with Matplotlib library PO2/

PSO5,6,7

AP P

CO9 Demonstrate how to work with missing data PSO5,6 AP P

CO10 Demonstrate how to reduce dimensionality using PCA and LDA

PO4/

PSO5,6

AP P





(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Data Science Introduction, Installation and Setup, Introduction to Data Pre-Processing, Importing Libraries. Im-

porting Dataset, Working with missing data, Encoding categorical data, Splitting dataset into train and test set,

Feature scaling, Summary

MODULE II Introduction to Simple Linear Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial

Regression, Support Vector Machine introduction and regression, Decision Tree, Random Forest, Evaluating the

model performance: R-Squared and Adjusted R-Squared, Analyzing Model Performance with R-Squared and

Adjusted R-Squared, Analyzing the Coefficients

MODULE III Classification: Logistic regression, KNN, Support vector machine, Naive Bayes, Decision Tree, Random Forest,

Cluster Analysis: Hierarchical cluster Analysis

MODULE IV Natural Language Processing: Introduction and installation, Tokenization, Stemming, Lemmatization, Stop

words, POS Tagging, Chunking, Named entity recognition, text classification

MODULE V Introduction to Artificial Neural Networks: The neuron, Activation function, Cost function, Gradient descent

and back-propagation algorithms, Convolutional Neural networks (CNN): Introduction, linking to Tesor Flow

Notebooks and building CNN

MODULE VI Data Analysis with Numpy, Introduction, NumPy Arrays: Indexing and Selection, NumPy Operations

Data Analysis with Pandas: Introduction, Pandas Series, Data Frames, Multi-index and index hierarchy, Work-

ing with Missing Data, Group by Function, Merging, Joining and Concatenating Data Frames, Pandas Opera-

tions, Reading and Writing Files

Data Visualization with Matplotlib

REFERENCES

1. McKinney, Wes. Python for Data Analysis. " O'Reilly Media, Inc.", 2013.

2. Sweigart, Al. Automate the boring stuff with Python: practical programming for total beginners. No

Starch Press, 2015.

3. Albon, Chris. Machine learning with python cookbook: Practical solutions from preprocessing to deep

learning. " O'Reilly Media, Inc.", 2018.

4. Beazley, David, and Brian K. Jones. Python Cookbook: Recipes for Mastering Python 3. " O'Reilly Me-

dia, Inc.", 2013.

5. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to

Build Intelligent Systems


1. Matthes, Eric, Python Crash Course, and No Starch Press. "Introduction to Python." Introduction to Py-

thon: An open resource for students and teachers (2017).

2. Swaroop C. H A Byte of Python by. - https://python.swaroopch.com/

Semester: II Course Code: FUS-CC-621 Credits: 3

COMBINATORIAL OPTIMIZATION


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the basics of Combinatorial optimization

techniques with the help of graph theory, matrix theory

and integer programming

PO3

PSO2,3,4,5,7

U C,P

CO2 Apply concepts and methods of modelling of real world

optimization problems in industry and other organiza-

tions

PO4

PSO2,3,4,5,7

AP C,P

PCO3

Understand the basic algorithmic design techniques

such as greedy, dynamic programming, divide and con-

quer , searching techniques

PO3 /

PSO5

U F,C

CO4 Understand the basics of analysis of algorithms and

algorithmic complexity

PSO2,5 U F,C

CO5 Apply routing algorithms for real-world applications PSO3,5,6,7 AP C,P

CO6 Apply the approximate algorithms for hard combinato-

rial optimization problems like TSP and Knapsack

PO3,4 /

PSO5,7

AP C,P




Bloom’s Cate-

gory


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I Integer Linear programming, Mixed-Integer Linear Programing Problems, Generic Combinatorial Op-

timization Problems, Formulation of standard models of integer and mixed-integer programming prob-

lems like Matching problem, Knapsack problem. Assignment problems. Set-covering problem, Set-

packing problem and Set-partitioning problem, Travelling Salesman Problem (TSP), Sequencing prob-

lems and other combinatorial Optimization Problems.

MODULE II Complexity of algorithms, Class P problems - Class NP- NP-hard problems, Searching algorithms –

BFS/DFS- Spanning tress, Minimal Spanning Tree Problem and Greedy Strategies, Data structure for

combinatorial optimization problems. Analysis of Heuristics

MODULE III

Network Theory: Max flow/Min cut problem, Ford-Fulkersen’s Augmenting Path Algorithms- Bipar-

tite Matching, Applications of Max-Flow Problem to Transportation Problems and bipartite matching

etc.- Minimum Cost-Flow Problems etc.

MODULE IV Shortest Path or Minimum Weight Path problems and Solution Strategies: Dijkstras Algorithm, Bell-

man-Ford Algorithms and Floyd- Warshall Algorithms

MODULE V Theory of valid inequalities. Methods for solving mixed-integer programing problems.Relaxations of

discrete problems. Cutting-plane, Branch and bound and Branch and Cut Method.

MODULE VI Approximate Algorithms for hard combinatorial optimization problems like TSP, Knapsack, Vertex

Cover tec.

REFERENCES 1. Alexander Schrijver: Theory of linear and integer programming. John Wiley

2. Papadimitriou and Steiglitz: Combinatorial Optimization : Algorithms and Complexity, ,

Dower Publishers

3. Woolsey L.A and G .L Nemhauser Integer and Combinatorial Optimization –– John Wiley


1. http://www.imada.sdu.dk/~marco/Teaching/AY2014-2015/DM554/Notes/dm554-main.pdf

2. https://ocw.mit.edu/courses/mathematics/18-433-combinatorial-optimization-fall-2003/lecture-

notes/


MODELLING AND SIMULATION


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the concepts of modeling and simulations

PO3,4/

PSO2, 7

U F,C

CO2 Decide the numerical methods used for simulations PSO2, 5 E F,C

CO3 Carry out numerical simulation of continuous systems PSO2-7 AP F,C,P

CO4 Carry out numerical simulation of discrete determini s-

tic systems

PSO2-7 AP F,C,P

CO5 Carry out numerical simulation of discrete stochastic

systems

PSO2-7 AP F,C,P

CO6 Carry out numerical simulation of Monte Carlo simu-

lations

PSO2-7 AP F,C,P

CO7 Apply computer packages Octave/MATLAB/R for

computer simulations

PO3,4 /

PSO5

AP F,C,P

(CL- Cognitive Level: R-remember, U-understand, AP- Apply, AN- analyses, E- evaluate, CR- cre-

ate,



Bloom’s Cate-

gory

Continuous Assessment Tests (percentage) Terminal Examina-

tion

(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I Model classification: conceptual, abstract, and simulation models. Heterogeneous models. Methodology of

model building. Simulation systems and languages, means for model and experiment description. Principles of

simulation system design. Checking model validity, verification of models. Analysis of simulation results.

MODULE II Numerical Solution of Ordinary Differential Equations. – Initial-value problems: linear multistep methods,

Runge-Kutta methods, large systems and method of lines; error estimation and control; continuous output; event

location. – Boundary-value problems: the shooting method, finite difference methods. – Stiffness.

MODULE III Continuous systems modeling and simulations. Overview of numerical methods used for continuous simulation

of ordinary differential equations, Usage of octave/MATLAB/R for solving ordinary differential equations.

MODULE IV

Discrete system simulation of deterministic models. Model time, simulation experiment control, steady state

conditions, case studies

MODULE V

Discrete system simulation of Stochastic models: Random number generation, Analyzing output processes,

Transient and steady-state behaviors, Output analysis for terminating simulations, Output analysis for steady-

state parameters, Output analysis techniques for steady –state simulations, Replication analysis, Batch-means

analysis, Regenerative analysis, Rules of stopping of a simulation, Variance reduction techniques. Simulation

queening models using R package.

MODULE VI Monte Carlo Simulations: Introduction, Monte Carlo Integration: Introduction, Classical Monte Carlo integra-

tion, Importance sampling, An arbitrary change of reference measure, Sampling importance resampling, Selec-

tion of the importance function.

REFERENCES

1. Geoffrey Gordon: System Simulation, PHI

2. Narsingh Deo: System simulation with digital computer. Prentice Hall PTR, 1983.

3. L. Burden and J.D. Faires, Numerical Analysis, 7th ed. (2001), Brooks/Cole.

4. U.M. Ascher and L.R. Petzold, Computer Methods for Ordinary Diferential Equations and Differentia-

lAlgebraic Equations (1998), SIAM.

5. Robert, Christian P., George Casella, and George Casella. Introducing monte carlo methods with R. Vol.

18. New York: Springer, 2010. (Chapter 3)


1. Fishwick P.: Simulation Model Design and Execution, PrenticeHall, 1995, ISBN 0-13-098609-7

2. Law A., Kelton D.: Simulation Modelling and Analysis, McGraw-Hill, 1991, ISBN 0-07-100803-9

3. Rábová Z. a kol: Modelování a simulace, VUT Brno, 1992, ISBN 80-214-0480-9


PRINCIPLES OF TECHNOLOGY MANAGEMENT


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand fundamentals of technology management

concepts

PO4 /

PSO1,2,4,7

U F,C

CO2 Understand the approaches for Management of Tech-

nology

PSO1,2,4,7 U F,C,P

PCO3 Understand the role of technology in Competitive Ad-

vantage

PSO1,7 U F,C

CO4 Apply the concepts of knowledge mapping and learning

process in an organisation

PO3 /

PSO1,7

U F,C,P

CO5 Understand technology and R&D strategy and decision

support systems

PSO1,7 U F,C

CO6 Evaluate the methods suitable for a given situation PSO1,7 U F,C,P


ate,



Bloom’s

Category


tion

(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Management of Technology: Definitions, scope and implications - concepts, definitions and issues –

what is technology, management as technology – scope of technology management – System model –

Integrated and wholistic models – Strategic, operational and management issues – classification of

technologies.

MODULE II

Approaches to technology management: basic definitions – Systems Approach – the technology Cy-

cle and the technology flow process – basic tenets for the management of Technology (MoT)

MODULE III

Role of technology in Competitive Advantage – what is competitive strategy, characteristics, com-

petitiveness, competitive advantages – shop around, generic and competitive strategies – value

chain, technology and creating competitive advantage – technology and low cost-low price – tech-

nology and high quality – technology and sooner availability.

MODULE IV

Knowledge mapping, knowledge imperative and learning processes in technology management: a p-

plication of knowledge mapping for technology management – methods, chronological, co-word

based, cognitive and conceptual mapping – knowledge imperative – learning process in an organisa-

tion

MODULE V

Technology and R & D strategy and Decision Support Systems (DSS): Pre -conditions for develop-

ment of an R & D strategy – technology at the enterprising level – techniques used in the develop-

ment of Decision Support systems (DSS) – multi attribute utility analysis, discriminant analysis, di s-

tance from target analysis – expert systems – development of a new DSS

MODULE VI

Technology management -projects- case studies

REFERENCES

1. Gaynor, Gerald H. ―Handbook of Technology Management‖, McGraw Hill, 1996.

2. Dorf, Richard C. The technology management handbook. CRC Press, 1998.

3. Gehani, R. Ray. Management of technology and operations. John Wiley & Sons, 1998.

4. Kropsu-Vehkapera, Hanna, Harri Haapasalo, and Jukka-Pekka Rusanen. "Analysis of technol-

ogy management functions in Finnish hightech companies." The Open Management Journal 2

(2009): 1-10.

5. Pavitt, Keith. What we know about the strategic management of technology. California, 1990.

6. Porter, Alan L. Forecasting and management of technology. Vol. 18. John Wiley & Sons,

1991.

7. Schilling, Melissa A. Strategic management of technological innovation. Tata McGraw-Hill

Education, 2005.

8. Tesar, George, Sibdas Ghosh, Steven W. Anderson, and Tom Bramorski. Strategic Technology

Management: Building Bridges between Sciences, Engineering and Business Management.

Singapore: World Scientific, 2008. Internet resource.

9. Zedtwitz, Maximilian . Management of Technology: Growth Through Business Innovation

and Entrepreneurship : Selected Papers from the Tenth International Conference on Manag e-

ment of Technology. Amsterdam: Pergamon, 2003. Print.

10. BurgeSmani, Robert A., and Steven C. Wheelwright. "Strategic management of technology

and innovation." READING 1.1 (2004).


1. http://www.ifm.eng.cam.ac.uk/research/ctm/ 2. http://www.tifac.org.in/


IPR AND PATENTS: LAW, PRACTICE AND CONSULTANCY


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the fundamentals on intellectual property

rights

PO4 /

PSO1,7

U F, C

CO2 Differentiate between copyright, trademark and patent PSO1,4,7 AN F, C

CO3 Understand the rules and procedure of patenting PSO1,7 U F, C

CO4 Understand procedure of protection of IPR related to an

innovation

PSO7 U F, C

CO5 Understand intellectual property management PSO7 U F, C

CO6 Create Indian patent application for given innovation PO2 ,4 /

PSO7

AP F, C, P




Bloom’s

Category


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Fundamentals and general introduction to the concept and philosophy of IPR – Evolution and rele-

vance of IPR in global and Indian scenario – Different types of IPR (Patents, Trademarks, Copy-

rights, Geographical Indications Industrial Designs and Trade secrets), Patentable Subject Matter

(Novelty, Non Obviousness, Utility, enablement and Best mode)

MODULE II

History of Indian Patent Protection, Rationale behind Patent System, Objectives and Advantages of

Patent System, and future challenges. Indian Patents Act 1970, Definitions and Key Terminology,

Types of Patent applications, Inventions not patentable (section 3 and 4).

Patent filing procedure in India (Patent Prosecution), Specifications (Provisional and Complete),

Claims- types of claims and legal importance of claims, Grant of patent, Rights of Patentee and co -

owners

MODULE III

Opposition- pre-grant opposition and post-grant opposition, Anticipation, Infringement, Compulsory

Licensing, revocation of patents, and power of Controller.

Patent filing procedure under PCT, advantages, patent search and literature

MODULE IV

Invention disclosure, Non disclosure agreements, Provisional applications - Prior art search , na-

tional and international applications rudimentary of d rafting a patent application -drafting and filing

patent-Registration of trademark, geographical indication, copy rights, enforcement of related rights,

and copyrights in the digital world -Fair use, exceptions, role of copyright societies and their func-

tions

MODULE V

Background, Salient Features and Impact of International Treaties / Conventions such as Paris Co n-

vention, Berne convention, World Trade Organization (WTO), World Intellectual Property Organiz a-

tion (WIPO), Trade Related Aspects of Intellectual Property Rights (TRIPS), Patent Co-operation

Treaty (PCT), Madrid Protocol

MODULE VI

Patent in validation process in India, IPR related to copyright, trade mark, trade secret and geographi-

cal indication, Patent application writing, Claim construction and claims.

REFERENCES 1. Ahuja V.K Law relating to intellectual property rights 2. Cornish W.R. Intellectual property, cases and materials 3. Ganguli Intellectual Property Rights : Unleashing the Knowledge Economy 4. Rajesh Asrani Intellectual Property Management in Techno-Economic Paradigm

5. Salil K. Roy Chaudhary Law of trademarks, copyrights, patents

6. Subbaram N.R. Demystifying Intellectual Property Rights

7. Draft manual of Patent Practice and Procedure -2008 , The Patent Office, India

8. Manual of Patent Office Practice and Procedure -2010

9. Original Laws Published by Govt. of India

10. Protection of Industrial Property rights by P.Das and Gokul Das

11. fda.org,wipo.int,patentlawlinks.com, hc-sc.gc.ca,ich.org,cder.org


LAB II: STATISTICAL COMPUTING WITH R


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the fundamentals of R package and various

data structures

PO3, 4 /

PSO3,6,7

U C, P

CO2 Differentiate how to import and export data in R PSO5,6,7 AN C, P

CO3 Create user defined functions for advanced analysis for

academia and industry

PSO5,6,7 AP C, P

CO4 Create R programs for advanced computing and data

analysis

PSO4,5,7 AN C, P

CO5 Analyse the usage of R packages for a given purpose PSO5,7 AP C, P

CO6 Create reports of the results of computations PO3, 4 /

PSO2,3,5,7

AP C, P


ate,



Bloom’s Cate-

gory


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create 20 20 20 20

COURSE CONTENT

MODULE I

Introduction to R and Rstudio, Statistical computing with R. The basic functionality of R. Using R

for calculations. Using R to calculate summary statistics on data. Using R to generate random nu m-

bers. Variable types in R. Numeric variables, strings and factors. Accessing the help system.

Usage of various data structures such as vectors, matrices, arrays, lists and dataframes. Accessin g

elements of these data structures, sub-setting vectors, slicing arrays and drilling down on lists. A

first look at the apply and lapply functions to apply functions to arrays and lists.

MODULE II

Options for bringing in data for analysis. Reading text files, reading from database connections and

reading from web sources. Many problems involve multiple data sources, so we will discuss merging

data sources in R using the join command.

Statistical modeling functions: lm and glm. Linear and generalized lin ear models (for example, lo-

gistic regression) are the workhorses of modern analytics. In this module we will illustrate the i m-

plementation of these functions in R and the usage of the formula syntax for model specification.

Prediction and model checking via residuals.

MODULE III

Writing user defined functions: A powerful features of R is the ability to write your own functions.

These functions may help pre-process data or implement specific computational algorithms. In this

module we will introduce the R function syntax and in particular the passing of variables into the

function, and argument handling.

Ther elegant ways to writing functions and more brute force approaches. We will describe a p-

proaches that make R more efficient in function evaluations, in particular vectorization. We will dis-

cuss the ‖...‖ notation that allows arguments to be passed on to other functions. We will discuss fun c-

tions that themselves take other functions as arguments.

MODULE IV Iterating with R. Logic and flow control: In order to prepare for simulation modeling it is important

to be able to control the flow of an algorithm. These functions include the if, for, while and break

constructs.

Simulation: The Bootstrapping and Monte-Carlo simulation in R. Sample command and the intro-

duction of random number generation from the canonical probability distribution functions.

MODULE V R packages : One of the great benefits of using R is the access to hundreds of add -ion packages. Dis-

cussion on the R ecosystem, and the illustration of R’s extensibility. We will illustrate this extens i-

bility through an example using the randomForest package and various other packages that work in

conjunction with it.

MODULE VI

R has a wide range of graphic abilities, from the low level graphical primi tives that can be used to

build more complicated graphics to high level routines such as lattice and ggplot2. This class will

explore some of these graphical capabilities.

Dynamic and web reporting: Knitr and Shiny. Running R as part of a business pipelin e -- the R ter-

minal.

Creating live reports and on-line presentations driven by the R language. Rterm shell that allows R

to be run as a batch process which is useful in embedding R into a business work flow.

REFERENCES

1. Wickham, Hadley, and Garrett Grolemund. R for data science: import, tidy, transform, visual-

ize, and model data. " O'Reilly Media, Inc.", 2016.

2. Baumer, Benjamin S., Daniel T. Kaplan, and Nicholas J. Horton. Modern data science with R.

CRC Press, 2017.

3. Venables, W. N. D. M. Smith and the R Core Team, An Introduction to R - Notes on R: A

Programming Environment for Data Analysis and Graphics (available at http://cran.r -

project.org/doc/manuals/R-intro.pdf)

SOFTWARE:

1. The R statistical software program. Available from: https://www.r -project.org/

2. RStudio an Integrated Development Environment (IDE) for R. Available from:

https://www.rstudio.com/

Semester: II Course Code: FUS-DE-626 Credits: 3

ARTIFICIAL INTELLIGENCE FOR TECHNOLOGY LEADERS


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the fundamentals of artificial intelligence PSO3,4 U F,C

CO2 Apply machine learning techniques PSO5 AP P

CO3 Understand the fundamental of NLP and create com-

puter codes for NLP applications based on Python

packages.

PSO4,5 U F,C,P

CO4 Understand business aspects of artificial intelligence PSO5,6,7 CR F,C

CO5 Understand ethical and legal aspects of artificial inte l-

ligence

PSO6,7 U F,C

CO6 Understand risk factors and the business opportunities

of artificial intelligence

PSO6, 7 U F,C


ate,



Bloom’s Cate-

gory


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create 20 20 20 20

COURSE CONTENT

MODULE 1

An Introduction to AI - Evolution of Artificial Intelligence, Different Types of AI, AI Impact and

Drive on Business Models, AI for Technology Leadership. Introduction to Data Science - Statistical

Analysis, Data Acquisition, Data Pre-processing and Preparation, Data Quality and Transformation,

Handling Text Data, Principles of Big Data, Big Data Frameworks – Hadoop, Spark and NoSQL,

Data Visualization, Sampling and Estimation, Inferential Statistics, R & Pyth on Programming Essen-

tials, Data Governance and Strategy, Integrating Data into Products, Building surveys and conduct

interviews to solicit feedback on prototypes, Linear Regression, Non -Linear Regression, Forecasting

models.

MODULE 2 Machine learning in business -Machine learning approaches, Machine learning system architec-

tures, Probabilistic reasoning for machine learning, Ethical use cases of machine learning in bus i-

ness, Key terms and concepts of deep learning, Neural Networks and back propagation, RN N, CNN,

GAN, Applications in predictive modelling.

MODULE 3

Natural language processing in business– Introduction to natural language understanding, Case

studies, Sentiment analysis, Application of deep learning to NLP, Speech recognition, Hidden

Markov Models, Chatbots, Natural language generation, Speech synthesis, NLP with Python, Bus i-

ness applications of NLP.

MODULE 4 Business intelligence and analytics – Essential aspects of business intelligence, Market Research,

Operational intelligence, Business analytics models, Business analytics at strategic, functional, ana-

lytical level, Business analytics tools.

MODULE 5 The quest for artificial intelligence –AI in the enterprise, Ethical and Legal Considerations in AI,

New generation AI projects, AI in Health care applications, AI in automation, AI in agriculture, Case

studies.

MODULE 6 AI in risk management -Impacts of AI in the erm framework, Benefits and opportunities for risk

managers, Relevant competences for the risk manager of tomorrow. The Future of Artificial Intelli-

gence - Making machines that can think, Speeding into business and marketing, Doctors in a box,

Smarter and safer travel, Interconnected world.

REFERENCES

Module 1

1. Data Science from Scratch: First Principles with Python 1st Edition by Joel Grus 2. R for data science : Import, Tidy, Transform, Visualize, And Model Data Paperback – 20

January 2017 by Hadley Wickham

3. Applied Artificial Intelligence: A handbook for business leaders by Mariya Yao

4. The quest for artificial intelligence a history of ideas and achievements by nils j. nilsson.

5. Thomas H. Davenport, The AI Advantage: How to Put the Artificial Intelligence Revolution

to Work, MIT Press, 2018 6. Lenahan, B.T., Artificial Intelligence: Foundations for Business Leaders and Consultants,

University of BRITISH COLUMBIA, 2019, ISBN : 9781989478004

7. John Medicine, Artificial Intelligence for Business: A Modern Business Approach, 2019

Module 2

1. A Course in Machine Learning by Hal Daumé III.

2. Medicine, J., Artificial Intelligence Business Applications: A New Approach to AI and Ma-

chine Learning in Modern Business and Marketing, for Beginners and Advanced, 2019,

ISBN: 1085989429, 9781085989428.

3. Dr. Jagreet Kaur, Navdeep Singh Gill, Artificial Intelligence and Deep Learning for Decision

Makers: A Growth Hacker's Guide to Cutting Edge Technologies, BPB Publications, 2019. 4. https://www.javatpoint.com/probabilistic-reasoning-in-artifical intelli-

gence#:~:text=Probabilistic%20reasoning%3A,logic%20to%20handle%20the%20uncertainty. 5. Neural Networks and Deep Learning, by Nielsen, M. A.

6. Deep Learning, by Goodfellow, I., Bengio, Y. and Courville A.

Module 3

1. The Great AI Awakening‖ by Lewis-Krause, G

2. Natural Language Processing from Scratch, by Collobert

3. Neural Network Methods for Natural Language Processing Synthesis Lectures on Human

Language Technologies, by G Golderg, Y.

4. Sentiment Analysis Tutorial, by Feldman, R

5. Artificial Intelligence: A Modern Approach, by Russell & Norvig, 2010

6. Complete Guide to Chatbots: Development to Promotion, by Maruti Labs.

7. Chatbot Fundamentals: An interactive guide to writing bots in Python, by Daly, L.

8. Natural language processing with Python by Steven Bird, Ewan Klein and Edward Loper

Module 4

1. Business intelligence and analytics by Drew Bentley.

2. Business analytics for managers: taking business intelligence beyond reporting by Gert H. N.

3. Key Business Analytics: The 60+ business analysis tools every manager needs to know

by Bernard Marr.

Module 5

1. The quest for artificial intelligence a history of ideas and achievements by nils j. nilsson.

2. AI and Machine learning in industry, by Beyer, D.

3. Why AI may be the next big privacy trend, by Jerome, J.

Module 6

1. https://www.eciia.eu/wp-content/uploads/2019/11/FERMA-AI-applied-to-RM-FINAL.pdf

2. The Future of Technology: What Is the Future of Artificial Intelligence?, 1st Edition by John

Allen.


ECONOMETRICS AND ECONOMIC FORECASTING


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the basic concepts in Economet-

rics PSO1,2,4,7 U C

CO2 Apply mathematical modelling using real life

data

PSO3,7 AP P

CO3 Evaluate the strength and validity of models PO4/

PSO1,7 E P

CO4 Apply Models in quantitative and qualitative

data

PO4/

PSO1,5,7 AN M

CO5 Apply models in time series data PO4/

PSO1,5,7 AN M

CO6 Apply softwares for mathematical modelling PO4/

PSO1,7 AN P, M


ate, KC- Knowledge Category: F-Factual, C- Conceptual, P-Procedural, M- Metacognitive)


Bloom’s Cate-

gory Continuous Assessment Tests (percentage) Terminal Examination

(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I Nature and Scope of Econometrics, Statistical Concepts: Normal distribution; chi-sq, t- and F-

distributions; estimation of parameters; properties of estimators; testing of hypotheses, Simple Li n-

ear Regression Model: Two Variable Case - Estimation of model by method of ordinary least squares;

properties of estimators; goodness of fit; tests of hypotheses; scaling and units of measurement; co n-

fidence intervals; Gauss-Markov theorem; forecasting.

MODULE II Multiple Linear Regression Model Estimation of parameters; properties of OLS estimators; goodne ss

of fit - R2 and adjusted R2 - partial regression coefficients; testing hypotheses - Violations of Clas-

sical Assumptions: Consequences, Detection and Remedies Multicollinearity; heteroscedasticity;

serial correlation

MODULE III Specification Analysis - Omission of a relevant variable; inclusion of irrelevant variable; tests of

specification errors.

MODULE IV Dummy variable technique – Use of dummy variables, regression with dummy variables – ANOVA

models, ANCOVA models, interaction effects, piecewise regression, deseasonalisation, Logit, probit

and Tobit models.

MODULE V Forecasting using Time series models: Smoothing and Extrapolation of Time series – Simple ex-

trapolation models, Smoothing and seasonal adjustment. Properties of stochastic Time series : Intro-

duction to stochastic time series models. Stationary and Non stationary Time series, Linear Timer

Series Models. Moving average models, ARIMA Models and Specification of ARIMA models.

MODULE VI Practical Application of Statistical Packages

REFERENCES 1. Christian Gourieroux, Econometric of Qualitative Dependent Variables, Cambridge Unive r-

sity Press, 2000.

2. Christopher Dougherty, Introduction to Econometrics, Oxford University Press, 3rd edition,

Indian Edition, 2007.

3. Goldberger, A., A Course in Econometrics, Harvard University Press.

4. Greene, W. H., Econometric Analysis, Prentice Hall.

5. Gujarati, D. N. and D.C. Porter, Essentials of Econometrics, McGraw Hill, 4th edition, I n-

ternational Edition, 2009.

6. Jan Kmenta, Elements of Econometrics, Indian Reprin t, Khosla Publishing House, 2nd edi-

tion, 2008

7. Kerry Patterson, An Introduction to Applied Econometrics: A time series Approach, Macmi l-

lan Press, London 2000.

8. Krishna, K.L. Econometric Applications in India, OUP, New Delhi 1999.

9. Robert. S.Pindiyck& Daniel Rubinfield, Econometric Models and Economic Forecasts, Mac-

Graw Hill, 1998.

10. Ruud, P., An Introduction to Classical Econometric Theory, Oxford University Press.

11. Stock, J.H. and M.W. Watson, Introduction to Econometrics (first edition), Addison-Wesley,

2003 (available at the COOP

12. Walter Enders, Applied Econometric Time Series, 2nd Edn., Wiley, 2008.

13. Wooldridge, J.M., Introductory Econometrics , South-Western College Publishing.


TOTAL QUALITY MANAGEMENT


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the concepts of quality system and

foundations of Total Quality Management

PO3,4/PSO4,6

U F,C

CO2 Understand various TQM Tools for Quality

improvement

PSO6,7 U F,C

CO3 Analyse conformance and non-conformance to

Quality standards

PO3,4/

PSO4,6 AN C,P

CO4 Understand the structure of Quality Organisa-

tion and implement teamwork for quality im-

provement

PO3/

PSO4,6 U F,C

CO5 Understand and apply the concepts of Quality

and Business Process Re-engineering

PO4/

PSO4,6 U, AP C,P

CO6 Analyse Total Quality Management case-

studies PO4/

PSO4,6 AN C

(CL- Cognitive Level: R-remember, U-understand, AP- Apply, AN- analyses, E- evaluate, CR-

create, KC- Knowledge Category: F-Factual, C- Conceptual, P-Procedural, M- Metacognitive)


Bloom’s Cate-


(percentage)

1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Quality System and Foundations of Total Quality Management - Components of quality-The total

quality management approach-Innovation, design and improvement-Product quality characteristics

and service quality characteristics-Quality parameters and specific dimensions of quality - Planning

for quality-Flowcharting-Detailed flow process charts and flow diagrams-Planning for just-in-time

(JIT) management-System design and contents-System documentation, implementation and assess-

ment

MODULE II

TQM Tools and the Improvement Cycle - Measurement of quality- Costs of quality-Tools and tech-

niques for quality improvement-Statistical process control-Quality improvement techniques in ser-

vice industries-Specific techniques for design, reliability, maintenance and process improvement

MODULE III

Conformance and Non-conformance to Quality Standards - Quality of design-Quality of confor-

mance to design-Control of non-conforming products - identifying and classifying non-conformance-

documenting non-conforming products- reinspection of repaired and reworked products -Corrective

and preventive action

MODULE IV

The Quality Organisation within an Organisation - People and the organizational structure-

Responsibilities and performance management -The relationship between the quality organization and

top management-Culture change through teamwork for quality improvement -Implementing teamwork

for quality improvement: the DRIVE model.

MODULE V Quality and Business Process Re-engineering - Beyond tools to total quality management-Stages in

the development of quality and related activities: Inspection- quality assurance-company-wide qual-

ity control- total quality management-Quality circles-Stages in the evolution of a company’s im-

provement capability - traditional approach- structured continuous improvement-goal-oriented con-

tinuous improvement- proactive/empowered continuous improvement -full continuous improvement

capability (the learning organization)

MODULE VI

TQM-projects- case studies

REFERENCES

1. Brown, S. Strategic Operations Management 2nd Edition Elsevier Butterworth -Heinemann

2005

2. De Toro I.J. Addison –Wesley Publishing Company 1992

3. Hoyle, D.ISO 9000 Quality Systems Handbook: 2nd Edition. Butterworth/Heinemann 1997

4. Oakland, J.S. Total Quality Management: the route to improving performance Butte r-

worth/Heinemann (1993)

5. Tenner, A.R. Total Quality Management: Three Steps to Continuous Improvement


COMPUTER AIDED DESIGN


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the philosophy and architecture of

Computer aided design

PO3/PSO4,6

U F,C

CO2 Understand application of computers in De-

sign and apply modelling techniques

PO3,4/PSO6 U F,C

CO3 Understand and apply basic elements of com-

puter graphics and transformations PO4/

PSO3,4,6 U, AP F,C,P

CO4 Understand concepts related to various re-

quirements such as software, interface re-

quirements as well as graphical kernel system

and data exchange standards

PO4/

PSO3,4,6 U F,C,P

CO5 Analyse linear and non and linear systems,

understand sensitivity models for computer

aided design and optimization methods in sys-

tems design

PO3,4/

PSO3,4,6 AN F,C, P

CO6 Understand and apply concepts on data struc-

tures and data base management PO4/

PSO3,6 U,AP F,C




Bloom’s Cate-


(percentage)

1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Computer aided design philosophy - Systems definition – Introduction to manufacturing- Brief his-

tory of CAD-Principles of Computer aided design and manufacturing. Computer aided design basics -

Advantages of CAD- Engineering Product Specification-Design modelling requirements-Paradigms

of Design - Architecture of CAD

MODULE II

Application of computers in Design-Engineering design-Design Drafting-CAD, Geometric modeling

- Surface modelling - Solid modeling

MODULE III

Basic elements of Computer Graphics- co-ordinate systems- 2D and 2D transformations- Homogene-

ous coordinates – Line drawing – Clipping – Viewing transformation

MODULE IV

Software requirements specification - Product functions - Design Requirements - Interface require-

ments - Specific requirements- design constraints - standards compliance –Graphical Kernal System

–OpenGL –Data exchange standards

MODULE V Computer method of linear and non-linear systems analysis - Sensitivity models for computer aided

design - optimization methods in systems design - Automated system design - Computer aided prob-

abilistic design.

MODULE VI

Data structures and data base management – Numerical consideration – Graphics -Introduction to

finite element modelling and analysis – General procedure for FEM, Finite element modeling, Gen-

eral structure of a FEM procedure

REFERENCES

1. Farin, Gerald E. Curves and surfaces for computer-aided geometric design: a practical code .

Academic Press, Inc., 1996.

2. Kalay, Yehuda E. Architecture's new media: principles, theories, and methods of computer-

aided design. MIT Press, 2004.

3. Lofberg, Johan. "YALMIP: A toolbox for modeling and optimization i n MATLAB."Computer

Aided Control Systems Design, 2004 IEEE International Symposium on . IEEE, 2004.

4. Micheli, Giovanni De. Synthesis and optimization of digital circuits . McGraw-Hill Higher

Education, 1994.

5. Mrozek, Zbigniew. "Computer aided design of mechatronic systems."International Journal of

Applied Mathematics and Computer Science 13 (2003): 255-267.

6. Prasad, Biren. Concurrent engineering fundamentals . Vol. 1. Prentice-Hall PTR, 1997.

7. Renner, Gábor, and AnikóEkárt. "Genetic algorithms in computer aided de sign." Computer-

Aided Design 35.8 (2003): 709-726.

8. Tien-Chien, Chang. Computer-aided manufacturing. Pearson Education India, 2008.

9. Upadhyay, Parag R., and K. R. Rajagopal. "FE analysis and computer -aided design of a

Sandwiched axial-flux permanent magnet brushless DC motor."Magnetics, IEEE Transactions

on 42.10 (2006): 3401-3403.

ONLINE SOURCES

1. http://nptel.ac.in/courses/112102101/ 2. http://nptel.ac.in/courses/Webcourse-contents/II

3. Delhi/Computer%20Aided%20Design%20&%20ManufacturingI/

4. http://fosscad.org/fc/ 5. http://www.design-skills.org/about_cad.html


ENVIRONMENTAL ENGINEERING


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand different scales of interaction be-

tween man and environment

PO4/PSO4,6

U F,C

CO2 Understand Environmental Impact Assessment

(EIA) and different methods of EIA

PO2,4/PSO4,6 U F,C

CO3 Apply Mathematical Modeling to analyse en-

vironmental engineering problems

PO3,4/PSO4,6 AP C,P

CO4 Apply environmentally sound technology for

Sustainable Development PO3,4/PSO4,6

AP C,P

CO5 Understand green technology and analyse pol-

icy options for environmentally sound tech-

nology in India

PO3,4/PSO4,6 U,AN C. P

CO6 Analyse case studies for EIA and environ-

mental engineering problems PO3,4/PSO4,6,7

AN C




Bloom’s Cate-


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Man and environment: scales of interaction - environmental systems - environmental legislation and

regulation – a materials Balance approach to environmental engineering problem solving - Hydrol-

ogy - rainfall analysis surface and ground water analysis - water resource management - water qual-

ity management

MODULE II

Pollution - abatement methods of air pollution. Noise pollution, forecasting and its abatement - Envi-

ronmental Impact Assessment (EIA) - different methods of EIA –

MODULE III

Mathematical Modeling of environmental engineering problems - System Dynamic Modelling

MODULE IV

Environmentally sound technology for Sustainable Development - technological options that mini-

mize the loss of biological diversity

MODULE V

Environmental policy (taxation/subsidy) as instruments for diffusion of green technology - transfer

of green technology to developing countries - policy options for environmentally sound technology

in India

MODULE VI

Case studies for EIA and environmental engineering problems

REFERENCES

1. Allen, David T., and David R. Shonnard. Green engineering: environmentally conscious de-

sign of chemical processes . Pearson Education, 2001.

2. Allenby, Braden R., and T. E. Graedel. Industrial ecology. Prentice-Hall, Englewood Cliffs,

NJ, 1993.

3. Anastas, Paul T., and Julie B. Zimmerman. "Peer reviewed: design through the 12 principles

of green engineering." Environmental science & technology 37.5 (2003): 94A-101A.

4. Billatos, Samir. Green technology and design for the environment . CRC Press, 1997.

5. Corbitt, Robert A. Standard handbook of environmental engineering . New York, 1990.

6. Dincer, Ibrahim, and Marc A. Rosen. Exergy: energy, environment and sustainable develop-

ment. Newnes, 2012.

7. Elzen, Boelie, Frank W. Geels, and Kenneth Green, eds. System innovation and the transition

to sustainability: theory, evidence and policy . Edward Elgar Publishing, 2004.

8. Hoffert, Martin I., et al. "Advanced technology paths to global climate stability: energy for a

greenhouse planet." science 298.5595 (2002): 981-987.

ONLINE SOURCES

1. http://www.aaees.org/

2. http://www.iema.net/

3. http://www.asce.org/Leadership-and-Management/Ethics/Code-of-Ethics/


ENERGY, ECOLOGY & ENVIRONMENT


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the concepts on origins of earth

PO4/PSO4,6

U F,C

CO2 Understand various energy resources and en-

ergy use pattern in different regions of the

world.

PO3,4/PSO4,6 U F,C

CO3 Understand about environmental degradation,

as well as primary and secondary pollutants

PO2,3,4/PSO4,6 U F,C

CO4 Evaluate energy resources and demands

along with the main sources of data and

methods for analysis

PO3,4/PSO4,5,6 E C,P

CO5 Understand energy policy principles and

evaluate future technologies

PO2,3,4/PSO4,5,6 U,E F,C,P

CO6 Evaluate scenarios related to energy, ecology

and environment management problems PO3,4/PSO4,6,7

E C,P




Bloom’s Cate-


(percentage)

1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Origins of earth - Earth's temperature and atmosphere - sun as a source of energy, nature of its radia-

tion - Biological processes, photosynthesis, Food Chains, Marine ecosystem, Ecosystem theories,

Archaeology.

MODULE II

Sources of energy, classification of energy sources, quality and concentration of energy sources,

characteristics temperature - Fossil fuels - coal, oil, gas, geothermal, tidal and nuclear energy - So-

lar, Wind, hydropower, biomass - Resources of energy and energy use pattern in different regions of

the world.

MODULE III

Environmental degradation, primary and secondary pollutants - Thermal and radioactive pollution,

air and water pollution from stationary and mobile sources, Biological effects of radiation, heat and

radioactivity disposal - Pollution abatement methods.

MODULE IV

Fundamentals of modern/future energy systems in terms of their technical properties and economic

and environmental impacts: Solar photovoltaic electricity generation, fuel cells and hydrogen for st a-

tionary and transport electricity generation and wind power. Estimation of energy resources and de-

mands along with the main sources of data and methods for analysis.

MODULE V

Energy demand, supply markets and competition. Energy policy principles and local, national and

regional examples-National and international regulatory and legal environments. Energy-economics-

environmental models of global impact. Cost/Benefit analysis. Private investment decision making -

Evaluating future technologies-Policy instruments and market mechanisms for carbon mitigation.

MODULE VI

Case studies for Energy, ecology and environment management problems

REFERENCES

1. BalaBhaskar (2013). Energy Security and Economic Development in India: a holistic a p-

proach.TERI Press, New Delhi.

2. Brown, L.R. PLAN B 4.0. (2013). Mobilizing to Save Civilization. The Earth Policy Institute,

WW Norton and Co., New York, 362pp.

3. Brown, L.R., Larsen, J. & B. Fischlowitz-Roberts 2002.The Earth Policy Reader. Orient

Longman, Hyderabad, 303 pp.

4. Cunningham, W.P& M.A. Cunningham 2004.Principles of Environmental Science.Inquiry and

Applications. McGraw Hill Higher Education, New York, 424 pp.

5. Herbert Girardet and Miguel Mendonça. A Renewable World: Energy, Ecology, Equality

Green Books

6. IUCN, UNEP & WWF 1991.Caring for the Earth, International Union for the Conservation of

Nature, Gland.

7. Kishore V V N (2009). Renewable Energy Engineering And Technology Principles and Pra c-

tice. TERI Press, New Delhi.

8. Mendonça, M., Jacobs, D., Sovacool, B. (2009).Powering the Green Economy: The Feed -in

TariffHandbook, Earthscan.

9. Moef (2011). Sustainable Development in India: Stocktaking in the run up to Rio+20Ministry

of Environment and Forests, Government of India.

10. Pahwa, P K and G K Pahwa (2014) Hydrogen Economy. TERI Press, New Delhi.

11. Ringwald, A. (2008). India: Renewable energy trends. Centre for Social Markets, Kolkata.

12. TERI-NCSC-CUFE-ZU-UNDP. 2014. Low Carbon Development in China and India: Issues

and Strategies (Advance Publication). To be published by TERI Press

(http://www.teriin.org/pdf/TERI-NCSC-CUFE-ZU_China-India-LCD-book-new.pdf) 13. WWF (2012).Living Planet Report 2012. WWF International, Gland.

14. WWF (2013).The energy report- India.WWF-India and The Energy and Resources Institute,

2013

ONLINE SOURCES

1. www.ceeindia.org (Centre for Environment Education)

2. www.cseindia.org (Centre for Science and Environment)

3. www.desd.org/ (Education for Sustainable Development)

4. www.earthcharterinaction.org/education/UNDESD.html

5. www.earthday.net/footprint/info.asp

6. www.indiaenvironmentportal.org.in) (India Environment Portal)

7. www.ipcc.ch/ (for all climate change reports)

8. www.mnre.gov.in/

9. www.sustainable-development.gov.uk 10. www.teriin.org/ 11. www.un.org/esa/sustdev/

12. www.unep.org/ 13. www.worldfuturecouncil.org/a_renewable_world.html 14. www.portal.unesco.org/education/en/


STRATEGIC MANAGEMENT


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the various strategies in strategic

management of a business PSO1,4 U F, C

CO2 Understand the various models of strategic

management

PSO1,2,4,5,6,7 U F, C

CO3 Analyse the various growth strategies imple-

mented by a business unit or corporate

PO4/

PSO1,3,4,5,6 U F, C, P

CO4 Understand the environmental analysis of stra-

tegic management

PO4/

PSO1,2,4,5,6 U F, C, P

CO5 Understand the evaluation, control and choice

of a strategy

PO4/

PSO1,4,5,6 U F, C, P

CO6 Understand the evaluation aspects on strategic

management

PO4/

PSO4,5,6,7 U C, P




Bloom’s Cate-


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I Organisational Decisions - characteristics of successful strategy - Strategic Business Unit - - Strate-

gic Management Process - Levels of Strategies- Corporate, Business and Operational level, Types of

Strategies-Functional Strategies, H. R Strategy, Marketing strategy, Financial Strategy, Operational

Strategy Benefits and Risks of Strategic Management - Formulation of Strategy and Strategic Im-

plementation.

MODULE II Models for Strategic Management - The product Life Cycle and Strategic Management - Evaluation

of Strategic Alternatives, Types of Strategic Alternatives like Portfolio Analysis and its techniques,

SWOT Analysis, Profit Impact of Market Strategy (PIMS) - Strategic Change, Corporate Renewal,

Internal and External causes of Organizational failures -Culture of Organization, Management of

Strategies and Cultures

MODULE III Growth strategies: Concentric expansion strategy - Vertical integration strategy - Diversification

strategy - Merger strategy - Takeover strategy - Joint venture strategy - Strategic alliance - Organiza-

tional change - innovation generation and innovation diffusion - strategic leadership - - corporate

governance in behavioral implementation MODULE IV Environmental analysis in Strategic Management - Business Environment, Components of Environ-

ment, Environmental Scanning - forecasting techniques available for environmental analysis - ETOP

- Strategy Implementation,

MODULE V Evaluation and Control Strategic control - Premise control, Implementation control, Financial per-

formance control, Budgetary control, Social performance control, - Softwares for Strategic Manage-

ment - Analysis of Strategies and Choice of Strategy - Business, Corporate and Global Strategies:

Practices and Issues

MODULE VI Case studies on various aspects of Strategic Management

REFERENCES

1. David Hunger,J. and Thomas L. Wheelen(2011) Essentials of Strategic Management, custom

edition, Prentice Hall.

2. Hill, Charles W. L. and Gareth R. Jones (2012), Strategic Management: An Integrated Ap-

proach, 3. Jauch Lawrance R, Business Policy and strategic Management, MacGraw Hill Co; 4. Mathur, U.C. Text book of strategic management, Macmillain India limited.


KNOWLEDGE MANAGEMENT


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the concepts of Knowledge Man-

agement (KM)

PO3,4/PSO4,6

U F,C

CO2 Understand the role of IT in KM

PO3,4/PSO4,6 U F,C

CO3 Understand and apply technologies to manage

knowledge

PO3,4/

PSO4,6 U, AP C,P

CO4 Apply tools kit for KM PO2,3,4/

PSO4,6 E C,P

CO5 Analyse knowledge futures PO3,4/

PSO4,6 E C. P

CO6 Evaluate various aspects of Knowledge Man-

agement in Organisations PO3,4/

PSO4,6,7 E C.P




Bloom’s Cate-


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

The Nature of Knowledge -Knowledge Management (KM) - concept and its conceptualization -

Knowledge Management Solutions - basic KM discipline, emerging trends, practices, institutionaliz-

ing best practices for KM, process orientation: different types of knowledge. Overview of Know l-

edge Management-Organizational Impacts of Knowledge Management -Factors Influencing Knowl-

edge Management

MODULE II

IT in KM; social capital, intellectual capital (measurement - key performance indicators and evalua-

tion) and critical success factors, customer value management; networked knowledge economy

MODULE III

Technologies to Manage Knowledge: Artificial Intelligence, Digital Libr aries, Repositories, etc. -

Preserving and Applying Human Expertise: Knowledge-Based Systems - Past History Explicitly as

Knowledge: Case-Based Systems - Knowledge Elicitation: Converting Tacit Knowledge to Explicit -

Discovering New Knowledge: Data Mining Text - KM & Text Mining

MODULE IV

Tools kit for KM: knowledge networker’s tools kit, knowledge teams tools kit, knowledge based e n-

terprise tools kit, enterprise tools kit - Knowledge Capture Systems: Systems that Preserve and For-

malize Knowledge; Concept Maps, Process Modeling, RSS, Wikis, Delphi Method, etc. - knowledge

Sharing Systems: Systems that Organize and Distribute Knowledge; Ontology Development Systems,

Categorization and Classification Tools, XML-Based Tools, etc. - Knowledge Application Systems:

Systems that Utilize Knowledge

MODULE V

Knowledge futures: knowledge markets, knowledge ethics and governance, knowledge scenario KM

matrix; learning and knowledge (differences in learning styles, corporate memory and learning) –

KM Audit -

MODULE VI

Case Studies on various aspects of Knowledge Management

REFERENCES

1. Amrit Tiwana (2002). The Knowledge Management Toolkit: Orchestrating IT, Strategy,

and Knowledge Platforms (2nd Edition). Prentice Hall.

2. Elias M. Awad, Hassan M. Ghaziri (2004). Knowledge Management.Prentice Hall.

3. Irma Becerra-Fernandez, Avelino Gonzalez, Rajiv Sabherwal (2004). Knowledge Man-

agement Challenges, Solutions, and Technologies (edition with accompanying

CD).Prentice Hall. ISBN: 0-13-109931-0.

4. Madanmohan Rao (2004). Knowledge Management Tools and Techniques: Practitioners

and Experts Evaluate KM Solutions . Butterworth-Heinemann.

5. Stuart Barnes (ed) (2002). Knowledge Management Systems Theory and Practice . Thom-

son Learning


COMPUTER AIDED DECISION SUPPORT SYSTEMS


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand information system analysis and

design

PO4/

PSO4,6

U F,C

CO2 Understand and apply models related to Deci-

sion Support Systems

PO3,4/

PSO4,6,7

U,AP F,C

CO3 Understand Enterprise Resource Planning Sys-

tems

PO4/

PSO4,6,7 U C,P

CO4 Evaluate and create entity relationship mod-

els, relational Data Bases by writing SQL que-

ries

PO3,4/

PSO4,6 E,CR C,P

CO5 Create user interfaces through programming PO2,3,4/

PSO4,6,7 CR C. P

CO6 Evaluate the application of decision support

system in manufacturing systems. PO3,4/

PSO4,6,7 E C,P




Bloom’s Cate-


(percentage)

1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Introduction to information system analysis and design, decision support systems, database manag e-

ment systems, query languages, user interfaces. User interface languages, usability designs and co n-

siderations, model-based management systems

MODULE II

Development of decision support models, basic simulation models, mathematical and empirical mo d-

els. Model validation and verification, algorithms for decision support, alternative analysis, and

knowledge- based systems, implementing EOQ models

MODULE III

Enterprise resource planning systems, manufacturing resource planning systems -Introduction to de-

cision support systems, decision theory, rational decisions, applicability.

MODULE IV

Database management systems, MySQL in Linux platform, Relational database concept -

relationships, normal forms, database design for complex systems - Database queries, query lan-

guages and query optimization for decision support systems, Implementing SQL through server side

scripting.

MODULE V

User interfaces, HTML+PHP as user interface (UI) designing tools - Server side programming, inter-

facing with MySQL etc.

MODULE VI

Usability considerations of UI, information gathering and presentation for decision support, manip u-

lation of query results. Application of decision support system in manufacturing systems.

REFERENCES

1. Caisson M. Vickery, The Use of Computer-Aided Decision Support Systems for Complex

Source Selection Decisions, PN Publications

2. Frada Burstein, Clyde Holsapple, Handbook on Decision Support Systems 1: Basic

Themes, Volume 1, Springer


INTELLIGENT MANUFACTURING SYSTEMS


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand concepts related to expert systems PO4/

PSO4,6

U F,C

CO2 Apply PROLOG PO3,4/

PSO4,6,7 AP F,P

CO3 Apply design planning techniques for systems

manufacturing

PO3,4/

PSO4,6,7 AP C,P

CO4 Apply systems concepts in product design and

development

PO3,4/

PSO4,6,7 AP C,P

CO5 Understand expert system shells and packages PO3,4/

PSO4,5,6,7 U C, P

CO6 Analyse case studies on Intelli-

gent Manufacturing Systems

PO3,4/

PSO4,6,7 AN C,P




Bloom’s Cate-


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Introduction to Expert system - Introduction to Artificial Intelligence, Expert Systems Overview;

Development of Expert Systems; Problem Presentation, Expert System Structure, Knowledge Bases

and Representation, Inference Mechanism, Probability and Fuzzy Logic, User Interface, Develop-

ment Cycle, Expert System Development tools.

MODULE II

Introduction to PROLOG; Syntax and operations, Data Structures, Backtracking and Cut, Input -

Output, Predicates, Logic; Classes and Expert System Approaches

MODULE III

Analysis, Design Planning and Object-Oriented Systems in Manufacturing; Systems in System De-

sign; Equipment Selection, Layout Design

MODULE IV

Materials Handling, Capacity Planning; Systems in Product Design and Development; Product D e-

sign, Feature Extraction and Recognition, Bar Codes and Coding of Components

MODULEV: System in Manufacturing Planning, Scheduling and Control; Group Technology, NC

Part Programming, Process Planning - Generative and Variant, Production Planning, Resource

Scheduling, Automatic Storage and Retrieval, Robot Trajectory Planning, Inspection; Applications

Exercises; Exposure to Expert System Shells and Packages, Manufacturability Evaluation.

MODULE VI

Case studies

REFERENCES

1. Andrew Kusiak, Intelligent Manufacturing Systems, Prentice Hall (1990) 2. Mohammed Jamshidi and Hamid R. Parsaei, Design and Implementation of Intelligent Man u-

facturing Systems: From Expert Systems, Neural Networks, to Fuzzy Logic (Prentice -Hall,

1995)

Semester: III Course Code: FUS-CC-631 Credits: 3

RESEARCH METHODOLOGY AND NEGOTIATED STUDIES


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand objectives of doing research. PSO1 R, U F, C

CO2 Formulate the research problem. PO1/PSO2 AP M

CO3 Distinguish appropriate research designs and

techniques.

PO3/ PSO3 E P

CO4 Identify methods of data collection and analysis

strategies.

PSO3 U C

CO5 Develop enhanced writing skills to prepare tech-

nical reports.

PO2/PSO4 AP P

CO6 Analyse qualitative research report according to

the evaluation criteria.

PO4/ PSO4 An M


ate,



Bloom’s Cate-


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I Science in distinction to technology; Technologies to manage the world outside and oneself; Met h-

odology, method and technique

MODULE II Commonsense and Science; Popular Knowledge and Scientific Knowledge- Objectivity and Subjec-

tivity; Value Neutrality; Law and Truth; Universality-Methodology-Method –Technique- Epistemol-

ogy; Ontology; Historical Ontology (overcoming essentialism) -Deterministic and Non-deterministic

Sciences-Human Reason and Modern Scientific Knowledge

MODULE III Survey of Research Methodologies: Rationalism, Idealism, Positivism, Post Positivism. Introduction

to major binaries of modern science, Subjectivity vs Objectivity, Realism vs Anti –realism, True vs

False, Scientific evolution vs Scientific Revolutions, Continuity vs Discontinuity, Deterministic vs

Probabilistic, Linearity vs Non –Linearity

MODULE IV Beyond the binaries and deterministic science - Methods: Deduction, Induction, Hypothetical Deduc-

tive -method, Explanation and Prediction, General and Particular, Cause and Effect. Scientific rev o-

lutions, Paradigms, Against Method, Epistemic shift; Frontiers of technology management research.

MODULE V Arrow of Time: Linearity and non-linearity; Irreversible Time- Postmodern Condition; Post-

structuralism; Deconstruction -From Methodology to Methodologies

MODULE VI Negotiated Studies. The students can select a review research paper in a standard journal and crit i-

cally review the contents and make a report on how to approach a research problem.

REFERENCES

1. Abraham Kaplan, 1964, Conduct of Inquiry, Chander Publishing Company, California.

2. Ann Majchrzak, 1984, Methods for Policy Research, Sage London

3. Carl G Hempel ―The Covering Law Analysis of Scientific Explanati on‖ in Leonard I

Krimerman (ed)

4. Catheriner Marsh, 1988, Exploring Data, Polity Press, Cambridge

5. Cohen and Ernest Nagel (ed) 1978, An Introduction to Logic and Scientific Method, Allied,

New Delhi

6. Jean-Francois Lyotard, The Post Modern Condition: A Report on Knowledge, The Manchester

University Press, Manchester

7. John Brewer and Albewrt Hunter, Multimethod Research: A synthesis of Styles, Sage Public a-

tions, London

8. Karl R Popper, ―The Hypothetical – Deductive Method and the Unity of Social and Natural

Science‖, in Leonard I Krimerman (ed)

9. Peter Clough and Cathy Nutbrown, A Students Guide to Methodology, Sage Publications,

London

10. Thomas S Khun, The Structure of Scientific Revolution, University of Chicago Press,

Chicago


INDUSTRIAL PROJECT

Learning Outcomes :On completion of the course, the student will be able to

CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Analyze existing/new problem in an indus-

try/organization, related technology manage-

ment, from a research point of view

PO2/

PSO3

AN F, C

CO2 Formulate appropriate methodology to address

the issues

PS03, PSO3 AP F,C,P

CO3 Develop suitable solutions / suggestions to pos-

sible improvements

PO3/ PSO6,

PSO

CR C, P


ate,


REFERENCES All literature relevant to the chosen Industrial Problem as suggested by the advisor of the industry

and required for the student.


DISSERTATION (STAGE I)

Learning Outcomes: On completion of the course, the student will be able to

CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Analyze the existing literature and identifying

knowledge gap PO1

PSO3

AN F, C

CO2 Formulate appropriate methodology to address

the issues

PS03, PSO6 AP F,C,P

CO3 Develop suitable solutions / suggestions to pos-

sible improvements

PO2, PO3

PSO6

CR C, P


ate,


COURSE CONTENT

Assign the student to develop a research plan and schedule for the semester/session and use this plan

as the basis for assignments and assessment of the student ’s performance.

An exhaustive review of literature is to be done and place the problem suitably in the overall realm

of research arena so that the exact gap identified. The student should have a clear idea of the obje c-

tives, tools and methodology for the problem at hand.

READING LIST

Necessary literature relevant to the chosen Research Problem as suggested by the advisor

Semester: III Course Code: FUS-DE-634 Credits: 3

SYSTEM DYNAMICS SIMULATION

Learning Outcomes: On completion of the course, the student will be able to

CO

CO Statement Mapping of COs

PO / PSO CL KC

CO1 Understand the fundamental concepts of sys-

tem dynamics simulation PO3/

PSO3,4,5,6

U F, C

CO2 Understand the modeling process PO4/

PSO5, 6

U F, C

CO3 Understand the building blocks of system dy-

namic models

PO4 /

PSO5, 6

U F, C

CO4 Understand the methodology of building cas-

ual loop diagram

PO3 /

PSO5, 6

U F, C

CO5 Develop system dynamic models PO3 /

PSO6, 7

CR C, P

CO6 Develop validation and testing strategies. PO3 /

PSO6, 7

AP C, P


ate,



Bloom’s Cate-

gory


tion

(percentage)

1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create 20 20 20 20

COURSE CONTENT

MODULE I

Complex and Dynamic Systems:-

System Thinking and Complex Systems, Learning in real world, Dynamic perspective

MODULE II

The Modeling Process:-

Problem Articulation, Formulation of Dynamic Hypothesis, Simulation model, Testing and

Evaluation

MODULE III

Structure and Behaviour of Dynamic Systems :-

Fundamental and Other Modes of Dynamic Behaviour, Feedback Structure, Growth and Limits

MODULE IV

Tools for Systems Thinking: Part I : -

Stocks, Flows and Accumulation, Identification of Stocks and Flows

Tools for Systems Thinking: Part II : -

Causal Loop Diagrams, Mapping Stocks and Flows Structure of Systems

Modeling Decision Making :-

MODULE V

Decision rules to govern the behavior of model variables, The concept of robust model, Formulation

flaws, Common Formulations

MODULE VI

Model validation and Testing :-

Difficulty in validation and verification, Model testing in practice, Types of tests

REFERENCES

1. Sterman, J. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex

World. Irwin/McGraw Hill. ISBN 0-07-231135-5.

2. Forrester, Jay W., 1968. Principles of Systems, (2nd ed.). Waltham, MA: Pegasus

Communications. 391 pp.

3. Forrester, Jay W. (1985). System Dynamics in Management Education (D -3721-1). System

Dynamics

4. Group, Sloan School. Cambridge, MA. Massachusetts Institute of Technolog y. June 3. 5 pp

5. Wolstenholme E. (1990), System Enquiry: A System Dynamics Approach. Wiley: Chichester.

6. Peter M. Senge, The Fifth Discipline, Doubleday Currency, 1990

7. Coyle, Robert Geoffrey. System dynamics modelling: a practical approach. Vol. 1. CRC

Press, 1996.

8. Sushil. System dynamics: a practical approach for managerial problems. Wiley Eastern

Limited, 1993.


INTELLIGENT AND KNOWLEDGE BASED SYSTEMS

Learning Outcomes :On completion of the course the student will be able to

CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Discuss the basic concepts of AI and its

various applications

PO3,4/

PSO4,6

U F,C

CO2 Classify the knowledge representation

models

PO3,4/

PSO4,6,7

AP C,P

CO3 Discuss the methodologies for developing

knowledge-based systems

PO3,4/

PSO4,6,7

U C,P

CO4 Examine neural networks and genetic al-

gorithms

PO3,4/

PSO4,6,5,7

AP C,P

CO5 Analyse the major application areas of AI PO2,3,4/

PSO4,6,5,7

A C. P

CO6 Operate softwares and packages for AI

based knowledge management

PO3,4/

PSO4,6,5,7

AP C,P


ate,



Bloom’s Cate-

gory


tion

(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Overview of the Artificial Intelligence - Basic concepts; Definition of AI; Background and past

achievements; Aims -Overview of application areas; Problems and problem solving: State space

search; Production rules; Logic; Heuristic search techniques; Generate and test; Hill climbing;

Search reduction strategies

MODULE II

Knowledge Representation -Representation models; Predicate logic; rules; Semantic nets; Frames;

Conceptual graphs; -Scripts; Fuzziness and uncertainty; Fuzzy logic; Statistical techniques for

determining probability

MODULE III

Methodologies - Methodologies for developing knowledge-based systems; The KBS Development

Life Cycle; Knowledge acquisition/elicitation; Management of KBS projects; Prototyping; Impl e-

mentation; Development environments

MODULE IV

Adaptive Approaches-Neural Networks; Architectures; Hopfield network; Multi-layer perception;

Feed forward; Back propagation; Genetic algorithms: Basic concepts; Population; Chromosomes;

Operators; Schemata; Coding, Rule induction; Basic concepts; Decision trees/rule sets

MODULE V

Major Application Areas-Expert systems; Natural language processing; Machine vision and robotics;

Data mining and intelligent business support; Internet based application

MODULE VI

Softwares and Packages for AI based Knowledge management

REFERENCES

1. Damiani, E., Howlett, R.J., Jain, L.C., Ichalkaranje, N., Knowledge-based Intelligent Infor-

mation Engineering Systems and Allied Technologies, edited (STM Publishing House, 2002)

2. Maria Virvou, L. C. Jain Intelligent Interactive Systems in Knowledge -Based Environments.

(Springer, 2008)


PLANNING AND MANAGEMENT OF HUMAN RESOURCES


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Discuss the approach to human resources man-

agement and planning

PO4/

PSO4,6,7

U F,C

CO2 Outline the recruitment and selection process

PO4/

PSO4,6,7

U F,C

CO3 Discuss training and placement of HR in diverse

organisations

PO4/

PSO4,6,7

U C

CO4 Discuss wage and salary administration

PO4/

PSO4,6

U C

CO5 Discuss capacity building and evaluation

PO4/

PSO4,6

U F, C

CO6 Infer case studies of various aspects of HR man-

agement.

PO4/

PSO4,6,7

AP C,P


ate,


Bloom’s Cate-


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

The approach to human resources management: Concept , objectives , scope, functions , importance -

Human Resource Planning - Meaning , Objectives, process, limitations, importance , responsibility

for human resource planning.

MODULE II

Recruitment and Selection - Meaning, sources of recruitment, selection process, induction - Social

aspects - Problems of selection.

MODULE III

Training and placement in diverse organisations - Meaning , identification of training and develop-

ment needs , methods of training and development, evaluation of training and development pr o-

grammes, significance of training and development , career development - Planning of human re-

sources - Manpower planning - Manpower Development - Utilisation of human resources - Perform-

ance appraisal : Meaning , process , methods, limitations, importance, internal mobility , separation -

MODULE IV

Wage and Salary Administration: Concept, objectives, factors influencing wage and salary admin i-

stration Job evaluation: meaning, principles, methods, limitations, importance , Systems of payment

: Time rate system ,piece rate system , Incentive payments .Fringe benefits, Executive compensation.

MODULE V

Industrial relations and personnel management functions, legal aspects, factories acts- Training

needs and planned refresher programmes at various levels for updating sills - Evaluation of training.

MODULE VI

Case Studies of various aspects of Human resource management.

REFERENCES

1. Dale Yoder & Paul Staudohar: Personnel Management & Industrial Relations Prentice-Hall,

Inc New York:

2. David A. De Cenze(2009) Fundamentals of Human Resource Management, Tenth Edition,

Wiley, John & Sons

3. Subba Rao P. (2013) Essentials of Human Resource Management & Industrial Rel a-

tions.Himalaya Publishing House.


STRUCTURE AND ANALYSIS OF COMPLEX NETWORKS


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Explain the graph theory concepts

PO4/

PSO4,6

U F,C

CO2 Discuss the properties of networks

PO3,4/

PSO4,6

U F,C

CO3 Examine the network metrics and properties

PO3,4/

PSO4,6

AN C,P

CO4 Differentiate the network visualisation methods

and algorithms

PO3,4/

PSO4,5,6

AN C,P

CO5 Analyse graph models

PO3,4/

PSO4,5,6,7

AN C,P

CO6 Analyse citation networks

PO3,4/

PSO4,5,6,7

AN C. P

CO7 Apply software packages for complex network

analysis

PO3,4/

PSO4,5,6,7

AN C,P

(CL- Cognitive Level: R-remember, U-understand, AP- Apply, AN- analyses, E- evaluate, CR- create, KC- Knowledge Category: F-Factual, C- Conceptual, P-Procedural, M- Metacognitive)

Bloom’s Cate-


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I Quick review of graph theory concepts, Random networks, Scale -free networks, Small world, Prefer-

ential attachment, fitness, resilience against random attacks

MODULE II

Network metrics and properties: degree, clustering coefficient, diameter, density, shortest paths, ce n-

tralities, communities, influence detection techniques and measures of user influence

MODULE III

Network visualization: graph formats; graph drawing; graph layout methods and algorithms

MODULE IV

Graph models: Erdos-Renyi random models (for comparison); small-world model; models of scale

free networks, preferential attachment, and Barabasi -Alberto model (other models as science ad-

vances).

MODULE V

Citation Networks-Detailed analysis of citation networks

MODULE VI

Softwares and packages for complex networks analysis

REFERENCES

1. Maarten van Steen:, Graph Theory and Complex Networks: an Introduction, 2010

2. Mark Newman: Networks an Introduction, OUP Oxford, 2010

3. Networks". Princeton University Press, 2006.

4. Newman, M., Barabasi, A. and Watts, D. ―The Structure and Dynamics of Networks‖

ONLINE SOURCES

1. http://www.cs.cornell.edu/~lwang/Wang10TAMC.pdf?attredirects=0

2. http://www.cs.usyd.edu.au/~visual/valacon/

3. https://www.vosviewer.com/

4. https://gephi.org/ 5. http://mrvar.fdv.uni-lj.si/pajek/


MANAGEMENT INFORMATION SYSTEMS


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Discuss the role of MIS in organisations

PO4/

PSO4,6 U

F,C

CO2 Examine the decision-making process

PO4/

PSO4,6,7 AP

C,P

CO3 Explain DBMS and analyse views of data

PO3,4/

PSO4,6 AN

C,P

CO4 Examine MIS design and various aspects in de-

tail

PO4/

PSO4,6,7 AP

C,P

CO5 Discuss ERP and software used in MIS

PO4/

PSO4,6,7 U

C. P

CO6 Analyse case studies to determine the future

scope of MIS

PO4/

PSO4,6,7 AN, E

C,P


ate,


Bloom’s Cate-


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

An overview of management - strategic management - Importance of information in managerial decision mak-

ing – Business environment – MIS – concepts – Role and impact of MIS in organisations

MODULE II

Concepts in planning and control – concepts of information – organisational structure – system concepts – deci-

sion making process – behavioural models of organisational decision making and decision maker – decision

making under stress – documenting and communicating decision rules.

MODULE III

Database management system – Logical and Physical view of data, Types of data bases

MODULE IV

System Development - Determination of information needs and sources – selection of conceptual design options

– detailed MIS design – forms and report design – physical systems design – physical data base design - proce-

dure development – programme development – testing – quality assurance. organizational aspects of implemen-

tation – operation and maintenance – post audit.

MODULE V

Enterprise Resource Planning, Softwares in MIS

MODULE VI

MIS Case studies – Future scope of MIS

REFERENCES

1. August W. Smith: Management Systems: Analysis and Applications: by (Dryden Press)

2. Gordon B. Davis & Margrethe H. Olsen: Management Information Systems – Conceptual Foundations,

Structure and Development (McGraw Hill)

3. Robert Murdick & Joel Ross: Introduction to MIS: by (Prentice Hall)


SOFTWARE PROJECTS MANAGEMENT


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Outline of Project Management

PO4/

PSO4 AP

F,C,P

CO2 Explain project management activities and planning

PO3,4/

PSO4,6 AN

F,C,P

CO3 Analyse project sequencing and scheduling

PO3,4/

PSO4,6,7 AP

C,P

CO4 Explain contract management

PO4/

PSO1,7 AN

C,P

CO5 Examine project life cycle stages

PO4/

PSO1,7 AP

C. P

CO6 Determine risk

PO4/

PSO1,7 E

C



Bloom’s Cate-


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I Project Formation and Appraisal - Project Management – an overview – Feasibility – Technical – Market and

Demand – Economic and Financial – Formulation of Detailed Project Reports

MODULE II

Project Definition – Contract Management – Activities covered By Software Project Management – Overview

of Project Planning – Stepwise Project Planning

MODULE III

Objectives – Project Schedule – Sequencing and Scheduling Activities –Network Planning Models – Forward

Pass – Backward Pass – Activity Float – Shortening Project Duration – Activity on Arrow Networks – Risk

Management – Nature Of Risk – Types Of Risk – Managing Risk – Hazard Identification – Hazard Analysis –

Risk Planning And Control.

MODULE IV

Creating Framework – Collecting The Data – Visualizing Progress – Cost Monitoring – Earned Value – Priori-

tizing Monitoring – Getting Project Back To Target – Change Control – Managing Contracts – Introduction –

Types Of Contract – Stages In Contract Placement – Typical Terms Of A Contract – Contract Management –

Acceptance

MODULE V

Project Life cycle – Initiating – Planning – Executing – Controlling – Closing

MODULE VI

Risk Management and Control

REFERENCES

1. Bob Hughes, Mikecotterell, ―Software Project Management‖, Third Edition, Tata

McGraw Hill, 2004.

2. Humphrey, Watts S. Managing the Software Process (Hardcover). Addison-Wesley Professional, 1989.

3. Jalote, ―Software Project Management in Practice‖, Pearson Education, 2002.

4. Royce, Walker. Software project management. Pearson Education India, 1998.

Semester: IV Course Code: FUS-CC-641 Credits: 16

DISSERTATION (STAGE II)

Learning Outcomes:On completion of the course, the student will be able to

CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Develop research plan

PO2,3,4/

PSO4,6

AN F, C

CO2 Carry out detailed procedures for data collection/

survey/methods, theory for analysis

PSO3,4/PSO4,6 AP F,C,P

CO3 Analyse the results/outputs

PO4/

PSO4,6

CR C, P

CO4 Prepare the report giving the inference of the re-

search/project

PO2,4/PSO5,6,7 AP, CR P,M


ate,


COURSE CONTENT

The student is assigned to develop the research plan and question as a continuation of Dissertation

(stage I) and schedule for the semester/session and use this plan as the basis for assignments and a s-

sessment of the student’s performance.

The Dissertation (Stage II) must contain the detailed procedures for data collection/ survey/methods,

theory and tools to be developed. The student should present the results/output and analysis of the

study before finalizing the report. The final report is to be prepared by incorporating the suggestions

after the presentations.

REFERENCES

All literature relevant to the chosen Research Problem as suggested by the advisor and required for

the student

Semester: I Course Code: FDS-GC-411 Credits: 2

FORESIGHT AND FUTURES RESEARCH


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the critical concepts in futures

studies

PO4/ PSO2, 4,

5

U F, C

CO2 Understand Basic Concepts and six Basic Pi l-

lars of Futures Studies

PSO2,5,6 U F,C,P

CO3 Apply methods of foresight and futures re-

search

PSO2, 6 AP C, P

CO4 Evaluate various aspects of scenario building PSO4,6 E C, P

CO5 Evaluate the implications of scenario building

and Opportunity Analysis

PSO2,4,6 E C, P

CO6 Evaluate suitability of foresight methods for

given situation

PO4 / PSO2,4,6 E C, P


ate,



Bloom’s Cate-

gory


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create

COURSE CONTENT

MODULE I

Review futures basics- Become acquainted with key concepts, terms, and perspectives of the futures

field.- Introduce some of the publications and organizations in the field and discuss futures skills -

Identify the major events and founders in the history of future studies - Discuss what it means to be

a futurist- Role of a futurist

MODULE II

Six Basic Concepts and six Basic Pillars of Futures Studies - Prominent Futures Schools

MODULE III Environmental Monitoring, Scanning-Identify and monitor key quantities and conditions that ind i-

cate the baseline occurring -Environmental scanning as the means to keep current on change within a

specific domain- Practice environmental scanning and assess how well each scanning hit fits the

ideal criteria -Use scanning hits as the empirical support for alternative plausible futures

MODULE IV

Creativity- Scenarios -The key concepts, terms, and approaches to creativity - A standard approach to

creative problem solving and a variety of other Approaches - Scenario Theory- Key concepts, terms,

and criteria of good scenarios-The use of scenarios in professional practice -A review of different

ways that people can build scenarios Strengths and weaknesses of various scenario approaches

MODULE V Implications and Opportunity Analysis - Drawing out the implications of scenarios using, brain-

storming, futures wheels, expert panels and judgment - Identify challenges of specific scenarios for

specific domains- Use those challenges to prioritize strategic issues that should be addressed - See

change as resulting from trends, events, issues and images - Pick up the weak signals of coming

change through environmental scanning - Setting up a good scanning system- Progress/Decline:

That social change is generally an improvement of the human condition; or alternatively, that soci e-

ties have descended from a better period to today. Development: That social change moves in a

definite direction, but that the direction is neutral—not necessarily any better or any worse than the

past, just different.

MODULEVI Case Studies on Scenarios and other Futures Techniques

REFERENCES

1. Bill Ralston & Ian Wilson, The Scenario Planning Handbook

2. Jerry Glenn & Ted Gordon (eds.), Futures Research Methodology V3.0, CD, Washington DC,

Millennium Project, 2012.

3. Pero Micic, The Five Futures Glasses, 2010

4. Thomas Chermack, Scenario Planning in Organizations: How to Create, Use, and Assess Sce-

narios, 2011

5. Trevor Noble (2000), Social Theory and Social Change, New York: St Martin’s Press.

ONLINE SOURCES

1. https://en.wikiversity.org/wiki/Introduction_to_Futures_Studies

2. http://www.csudh.edu/global_options/IntroFS.HTML

3. https://cals.arizona.edu/futures/

Semester: III Course Code: FDS-GC-431 Credits: 2

PARALLEL PROGRAMMING WITH MPI


CO

CO Statement

Mapping of COs

PO/ PSO CL KC

CO1 Understand the concepts of parallel programming PSO3,4 U F,C

CO2 Understand different types of Parallel Systems PSO5 U F,P

CO3 Create parallel programs using MPI PSO4,5 CR F,C,P

CO4 Rewrite Sequential Program to Parallel Program PSO5,6,7 CR F,C,P


ate,



Bloom’s Cate-

gory


tion

(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create 20 20 20 20

COURSE CONTENT

MODULE I

Introduction to Parallel Processing -What is Parallel Processing?-The Goals of Parallel Processing -

Pros and Cons of Parallel Processing -Sequential Limits -Why Parallel Processing -Simplified Ex-

amples -Applications -History of Supercomputing

MODULE II

Types of Parallel Systems-SISD - Single Instruction stream over a Single Data Stream Single In-

struction stream over a Single Data Stream -MISD - Multiple Instruction stream over a Single Data

stream -SIMD - Single Instruction, Multiple Data Stream MIMD - Multiple Instruction, Multiple

Data Stream

MODULE III

What is MPI -Basic Idea of MPI -When Use MPI -Getting started with LAM -MPI Commands -MPI

Environment -MPI Functions Specifications -MPI Datatypes

MODULE IV

Parallel Programming using MPI -Communication Strategies-Point to Point Communication –

Collective Communication -Performance Evaluation

MODULE V

Demonstration and Writing of Simple MPI Programs

MODULE VI

Rewrite Sequential Program to Parallel Program, Strategies to rewrite Sequential Program

REFERENCES

1. Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar, Introduction to Parallel Co m-

puting. Second Edition. Addison-Wesley, 2003 (ISBN 0 201 64865 2).

2. Gropp, William, Ewing Lusk, and Anthony Skjellum. Using MPI: Portable Parallel Pro-

gramming with the Message Passing Interface. 2nd ed. Cambridge, MA: MIT Press, 1999.

ISBN: 9780262571326. 3. Gropp, William, Steven Huss-Lederman, Andrew Lumsdaine, Ewing Lusk, Bill Nitzberg, Wi l-

liam Saphir, and Marc Snir. MPI: The Complete Reference (Vol. 2) - The MPI-2 Extensions.

2nd ed. Cambridge, MA: MIT Press, 1998. ISBN: 9780262571234. 4. Selim G . Akl, The Design and Analysis of Parallel Algorithms, Prentice -Hall, Inc. 1989.

5. Snir, Marc, and William Gropp. MPI: The Complete Reference (2-volume set). 2nd ed. Cam-

bridge, MA: MIT Press, 1998. ISBN: 9780262692168. 6. Snir, Marc, and William Gropp. Using MPI-2: Advanced Features of the Message Passing In-

terface. Cambridge, MA: MIT Press, 1999. ISBN: 9780262571333.


1. http://openmp.org/wp/

2. http://www.mpi-forum.org/

3. https://computing.llnl.gov/tutorials/mpi/

4. https://computing.llnl.gov/tutorials/parallel_comp/

Semester: Any Course Code: FDS-GC-A1 Credits: 2

SCIENTIFIC RESEARCH PAPER WRITING


CO

CO Statement

Mapping of COs

PSO CL KC

CO1 Distinguish different types of research, their audiences and

how research material might be effectively presented

PSO3 U F,C

CO2 Prepare scientific and technical papers, and presentations. PSO3 CR F,P

CO3 Format documents and presentations to optimize their vi s-

ual appeal when viewed in-press, as a podcast or au-

dio/video file format on the internet, or through personal

presentations to an audience

PSO3 CR F,C,P

CO4 Effectively use LaTeX for professional documents PSO3 CR F,C,P

CO5 Accept constructive criticism and use reviewers’ comments

to improve quality and clarity of written reports and pres-

entations.

PSO3 U F,C




Bloom’s Cate-

gory


(percentage) 1 2 3



Apply 40 40 40 40

Analyse 10 10 10 10


Create 20 20 20 20

COURSE CONTENT

MODULE I

Introduction into Research, Types of scientific communication with examples, Scientific Literature,

Searching the scientific literature, Plagiarism and how to avoid it

MODULE II

Beginning to write - Establishing your constraints, Organizing your writing, Preparing outlines, Standard formats

for scientific papers, research projects and theses, Style guides. Content - Creating a literature review, Preparing

other sections of a research report (abstract, introduction, materials and methods, results and discussion, conclu-

sions), Including and summarizing research data.

MODULE III

Style and grammar - Scientific writing style, Passive vs. active voice Avoiding excessive wording, Grammar,

Avoiding misuse of words, When to use footnotes. Reference citations - How to use references - Within the

text - How to make lists of references, Citation Network Analysis.

MODULE IV

Revising - Dealing with revisions, Accepting criticism, Making sense of reviewers’ comments, Making the

changes, What to do if you don’t agree with reviewers’ comments. Other communication - Other types of scien-

tific writing like research proposals, creating a fact sheet/bulletin, articles for popular press, memos, letters and

emails.

MODULE V

Computer skills - LaTex.

MODULE VI

Presentations - Organization and formats for posters, Oral Presentations, Designing and preparing

slides for an oral presentation.

REFERENCES

1. How to Write and Publish a Scientific Paper. 6 Edition. Authors: Robert A. Day and Barbara

Gastel. ISBN: 0-313-33040-9

2. Alley, M. 2003. The Craft of Scientific Presentations: Critical steps to succeed and critical e r-

rors to avoid. Springer, NY. 241 pages. ISBN:0-387-95555-0.

3. Lamport, Leslie. LATEX: a document preparation system: user's guide and reference manual.

Addison-wesley, 1994.

REFERENCES 1. https://www.sharelatex.com/

Documents

M. Tech. Technology Management