Data Analysis and Decision Making Course | Multivariate Analysis IIT Kanpur
Course Details
| Exam Registration | 518 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Management Studies, Operations |
| Credit Points | 3 |
| Level | Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 19 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Mastering Data-Driven Decisions: An In-Depth Look at Data Analysis and Decision Making - I
In today's data-centric world, the ability to analyze complex information and extract actionable insights is paramount for effective leadership and strategic planning. The postgraduate course Data Analysis and Decision Making - I (DADM-I), offered by the prestigious Indian Institute of Technology Kanpur, stands as a cornerstone for professionals aiming to harness the power of multivariate statistical analysis. This 12-week program, the first in a three-part series, is meticulously designed to bridge the gap between theoretical statistics and practical, real-world application.
Meet the Expert: Your Guide to Advanced Analytics
The course is led by Prof. Raghu Nandan Sengupta, a distinguished academic with a formidable background in Operations Management. Prof. Sengupta holds a PhD (FPM) from IIM Calcutta and brings a rich research portfolio, with publications in renowned journals such as the European Journal of Operational Research, Quantitative Finance, and Computational Statistics & Data Analysis. His international exposure, including fellowships at Princeton University (USA), Warsaw University (Poland), and TU Dresden (Germany), ensures a global perspective on analytical techniques. At IIT Kanpur, he teaches critical subjects like Probability & Statistics, Financial Risk Management, and Management Decision Analysis, making him the ideal mentor for this rigorous course.
Who Should Enroll in This Course?
This course is specifically tailored for postgraduate students and professionals seeking to deepen their analytical prowess. The intended audience includes:
- Masters in Business Administration (MBA)
- Masters in Economics, Statistics, or Mathematics
- Masters in Industrial Engineering & Operations Research
- PhD scholars in related fields
Prerequisites: A foundational understanding of Probability & Statistics and basic Operations Research is recommended to fully grasp the advanced concepts covered.
Course Philosophy: Application Over Theory
DADM-I is explicitly designed to be practical and application-oriented. It moves beyond abstract formulas to focus on how multivariate tools solve actual business and operational problems. This approach ensures that students don't just learn methods but understand how to apply them in scenarios faced by industries like manufacturing, chemical, steel, and cement, which are explicitly noted as supporting industries for this skillset.
A Week-by-Week Journey into Multivariate Analysis
The 12-week curriculum is a comprehensive journey through the landscape of multivariate statistics:
| Week | Topic |
|---|---|
| Week 1-2 | Introduction to Multivariate Analysis, Joint & Marginal Distributions |
| Week 3-5 | Core Distributions (Multivariate Normal, t, Wishart) & Copula Methods |
| Week 6-8 | Regression Models, Factor Analysis, MANOVA/MANCOVA |
| Week 9-12 | Advanced Techniques: Conjoint, Cluster, & Discriminant Analysis, Multidimensional Scaling, Structural Equation Modeling (SEM) |
This structure ensures a logical flow from fundamental probability distributions to sophisticated modeling techniques used for segmentation, forecasting, and understanding latent structures in data.
Essential Reading for the Aspiring Analyst
The course draws upon a robust reading list from seminal texts in the field, ensuring academic depth. Key references include:
- Anderson, T. W. - An Introduction to Multivariate Statistical Analysis (a classic textbook)
- Johnson, R. A. and Wichern, D. W. - Applied Multivariate Statistical Analysis (known for its practical focus)
- Härdle, W. K. and Simar, L. - Applied Multivariate Statistical Analysis
- Bollen, Kenneth A. - Structural Equations with Latent Variables (crucial for SEM)
Why This Course is a Career Catalyst
Completing DADM-I equips you with more than just statistical knowledge; it provides a framework for intelligent decision-making. You will learn to:
- Handle and interpret datasets with multiple interdependent variables.
- Build predictive models using multiple regression.
- Segment markets or products using Cluster and Discriminant Analysis.
- Uncover hidden factors driving observed phenomena with Factor Analysis and SEM.
- Make robust decisions in the face of uncertainty and complexity.
As the first part of a trilogy (followed by DADM-II on other decision models like DEA/AHP, and DADM-III on Operations Research tools), this course lays the essential statistical foundation for anyone serious about excelling in data-driven roles in management, consulting, analytics, or research. It represents a significant investment in developing the critical skill set needed to transform raw data into strategic advantage.
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