Visiting Professors in the following months

Raghu Nandan SENGUPTA

Course title: Applied Design of Experiments

Programme for which the course is offered: BA

Registration: Students Office

Brief course description             

The course will teach the students different methods of experimental design and their respective applications for various practical problems ranging from engineering, social sciences, management sciences, etc.

Full course description               

Introduction to Design of Experiments with examples, Sampling & Sampling Distributions, Inferences about means, Concept of Randomized Designs, Simple Comparative Experiments, Paired Comparison, Checking of Model Adequacy, Determining Optimal Sample Sizes, Single/Multiple/Non-parametric Factor Analysis of Variance (ANOVA), Randomized Complete Block Designs (RCBD), Latin Square Designs, Factorial Designs, Two Factor and Multi Factor Design, 2k Factorial Design, Two Level Fractional Factorial Design,  Two Level Fractional Factorial Design, Regression Models, Response Surface Methods, Random Factors Experiments, Nested Designs, Split-plot designs, Robust Design, Application to Industrial Problems, Process Optimization, etc., Practicals using statistical packages.

Prerequisites   

Formal prerequisites: Probability, Mathematical Statistics, Mathematical Analysis, Linear Algebra

Other prerequisites: Understanding concepts related to techniques in descriptive statistics, simple concepts related to randomized experiments, interval/hypothesis testing, sampling rules, Combinatorics, etc.

Learning outcomes       

Knowledge: At the end of this course the student will be able to design an experimental study with cognizance of the competing risks and benefits of available choices, carry out an appropriate statistical analysis of the data, and properly interpret and communicate the analyses.

Abilities: Apart from this the student can utilize the knowledge gained in the course to different practical as well as theoretical problems. This will enable him/her to appreciate the underlying essence of this course. Apart from this he/she will also be in a position to identify difference practical constraints faced in designing proper experimental designs.

Assessment  methods and criteria        

•             Class Test/Quizzes (50%)

•             Take Home Assignment (25%)

•             Written examinations (25%)

Type of examination    

•             Class Test/Quizzes

•             Take Home Assignment

•             Written examinations

Type/form of class: Lecture + Problem Solving Sessions (As applicable if any) – Total of 30 hours

Language of instruction: English

Bibliography    

Text Book

Montgomery, D. C. (2001), Design and Analysis of Experiments, John Wiley & Sons. Inc. ISBN: 0-471-31649-0.

Other References

Brown, D. R., and Michels, K. M., (1962), Statistical Principles in Experimental Design, McGraw-Hill, ISBN: 978-0070709829.

Box, G. E. P., and Draper, N. R. (1987), Empirical Model-Building and Response Surfaces, Wiley, ISBN: 978-0471810339

Box, G. E. P., Hunter, W. G., and Hunter, J. S. (1978), Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, John Wiley & Sons. Inc. ISBN: 0-471-09315-7.

Cochran, W. G., and Cox, G. M., (1957), Experimental Designs, Wiley, ISBN: 978-0471545675.

Dean, A. M. and Voss, D. T. (1999), Design and Analysis of Experiments (Springer text in Statistics), Springer Science + Business Media, Inc. ISBN: 0-387-98561-1.

Fisher, R. A., (1966), The Design of Experiments, Hafner, ISBN: 978-0028446905.

Hinkelmann, K., and Kempthorne, O., (1994), Design and Analysis of Experiments (Vol I), Wiley, ISBN: 978-0-471-72756-9.

Pukelsheim, F., (1993), Optimal Design of Experiments,Wiley, ISBN: 978-0898716047.

Wu, C. F. Jeff and Hamada, M. S. (2000), Experiments: Planning, Analysis and Optimization, Wiley, ISBN: 978-0-471-69946-0.

Raghu Nandan SENGUPTA

Course title       Project Financing and Management

Programme for which the course is offered:     BA / MA

Registration: Students Office

Brief course description

Concepts related to Project Management, Financing of Big Projects, IRR, etc., will be covered in details. Students will also learn about different techniques used in Project Management like PERT, CPM, GERT, Q-GERT, and how they are used for different complicated project schedules encountered in practice. Ideas related to how different schemes of financing may be modeled for a variety of projects will also be discussed in details

Full course description               

Inroduction to Project Finance, Generation and Screening of Project Ideas, Project Appraisal and Evaluation, Project Finance as a Risk Management Techniques, Financial Projections, Investment Criteria, Cost Benefit Analysis, Project Finance, Financing Infrastructure Projects, Sources of Finance, Project Characterisics, Multilateral Project Financing, Consortium Financing, Venture Capital, Risk Analysis, Project Life Cycle, Valuing Projects, Cash Flow Problems, Project Leasing, Credit Risk in Project Finance, Techniques for Project Management (PERT, CPM, GERT, Q-GERT).

Prerequisites   

Formal prerequisites:    Basic Accounting & Finance, Basic Probability Distributions, Concepts of Uncertainty, etc.

Other prerequisites:      Understanding concepts related to techniques in utility theory and Risk Management, Hypothesis testing, Interval estimation, Basic Optimization, etc.

Learning outcomes       

Knowledge: At the end of this course the student will be able to understand any project from view of how financing is done, He/She will be able to model the main steps of project management design, and also understand how the concepts of Finance, considering the time value of money can be applied to any Project. The student will also learn about different risk mitigating techniques and how they may be used in big projects which are for longer duration, where uncertainty is very important.

Abilities: The student can utilize the knowledge gained in the course to formulate and analyze different practical as well as theoretical problems encountered in projects. They can also understand how different financing schemes may be utilized for projects. Apart from that the student can identify risk and techniques for allocation of the same, understand how to structure financing requirements for a project, analyze contractual arrangements and finally deal into issues of project default and remedies for the same.

Assessment  methods and criteria        

•             Class Test/Quizzes (25%)

•             Take Home Assignment (50%)

•             Written examinations (25%)

Type of examination    

•             Class Test/Quizzes

•             Take Home Assignment

•             Written examinations

Type of course Elective

Type/form of class         : Lecture + Problem Solving Sessions (As applicable if any) – Total of 30 hours

Language of instruction: English

Bibliography    

Text Book

Morris, P. W. G.,  and Pinto, J. K., The Wiley Guide to Managing Projects, 2004, JohnWiley & Sons, ISBN: 9780471233022.

Gatti, S., (2008), Project Finance in Theory and Practice, Academic Press, ISBN: 978-0-12-373699-4.

Other References

Moder, J. J., Philips, C., R. and Davis, E. W., (1983), Project Management with CPM, PERT, and Precedence Diagramming, Van Nostrand Reinhold, ISBN: 0442254156.

Pritsker, A., and Alan. B., (1983), Management Decision Making, Englewood Cliffs, Prentice-Hall, ISBN: 0135481643.

Wiest, J. D. and Levy, F. K., (1970), A Management Guide to PERT/CPM with GERT/PDM/DCPM and Other Networks, Prentice Hall of India, ISBN: 978-81-203-0132-0.

Lewis, R., Project Management, McGraw-Hill, 2006, ISBN 0-07-147160-X.

Phillips, J., PMP Project Management Professional Study Guide, McGraw-Hill, 2003. ISBN 0-07-223062-2.

Chandra, P., (2009), Projects: Planning, Analysis, Financing, Implementation, and Review by Prasanna Chandra, Tata McGraw Hill Publication, ISBN: 978-0070680081.

Introduction to Advanced Quantitative Methods for Data Scientists
Instructor: Prof. Taps Maiti, Michigan State University
Programme for which the course is offered:     MA (DS. IiE II st.)/PhD
Registration: Genowefa Smagała ?$=v_t1E@V[~GANCno59zm]#[}t'XGVx}WNJdP3FLTT#FkZ

Course Description: The main objective of this course is to introduce advanced statistical techniques for quantitative research in social and behavioral sciences. The course will have effective blend of theory, methods and applications. Each session will end up with practical problems and implementation of R codes. The course will include topics from regression analysis, nonparametric and nonlinear models and statistical machine learning. The tentative topics include High Dimensional Regression Techniques, PCR and PLS; Model selection, Penalized regression for high-dimensional model selection including, LASSO, RIDGE; Classification techniques: Logistic regression, Linear Discriminant Analysis, Classification for high dimensional data; K-mean clustering.  The depth and number of topics covered will depend on time and student preparedness.

Reference Book: The Elements of Statistical Learning by Hastie, Tibshirani and Friedman.

Evaluation: Group Project Presentation and Preparing concept papers. The number of groups will depend on the total number of students. Each group required to develop a theory or empirical project. Further each group required to develop a concept paper targeting a real life big data problems.

Tentative schedule:

Day 1: 6 hours

9:00-10:30: Introducing data complexity and their statistical implications by examples and connection to BIG data. Plan for course related activities.

10:30-11:00:  BREAK

11:00-12:30: Basics of Regression

12:30-2:00PM: Lunch

2:00-3:30PM: Statistical Model building I

3:30-4:00PM: Break

4:00-5:30PM: Data Lab

Day 2: 6 hours

9:00-10:30: Statistical Model building II

10:30-11:00:  BREAK

11:00-12:30: Logistics Regression and basic algorithm for solving nonlinear model

12:30-2:00PM: Lunch

2:00-3:30PM: Nonparametric regression and Neural Network modeling.

3:30-4:00PM: Break

4:00-5:30PM: Data Lab

Day 3: 6 hours

9:00-10:30: Classification – Logistic, LDA, KNN

10:30-11:00:  BREAK

11:00-12:30: PCR, PLS and Unsupervised learning

12:30-2:00PM: Lunch

2:00-3:30PM: Unsupervised learning, K-mean clustering, model-based clustering

3:30-4:00PM: Break

4:00-5:30PM: Data Lab

Day 4: Break, work on your presentation

Day 5: 3 hours

9:00-noon: Presentations by the students

**Depending on time and interest, topics may be included or excluded.