Statistical Inference Course | IIT Delhi | Prof. Niladri Chatterjee | Mathematics
Course Details
| Exam Registration | 77 |
|---|---|
| Course Status | Ongoing |
| Course Type | Core |
| Language | English |
| Duration | 8 weeks |
| Categories | Mathematics |
| Credit Points | 2 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 13 Mar 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 16 Feb 2026 |
| Exam Date | 28 Mar 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Master the Core of Data Science: A Comprehensive Guide to Statistical Inference
In an era driven by data, the ability to draw reliable conclusions from information is paramount. Statistical Inference forms the rigorous mathematical backbone of this process, enabling us to move beyond mere data description to making predictions, testing theories, and supporting decisions under uncertainty. Whether you're a student, a researcher, or an aspiring data scientist, a solid grasp of inference is non-negotiable.
We are thrilled to present a detailed, 8-week course on Statistical Inference, meticulously designed and taught by a distinguished authority in the field. This course is your gateway to mastering the principles that power modern data analysis and machine learning.
Your Expert Instructor: Prof. Niladri Chatterjee
Learning from the best accelerates your journey. This course is led by Prof. Niladri Chatterjee from the prestigious Indian Institute of Technology (IIT) Delhi.
- Position: Professor in the Department of Mathematics and Chair Professor in Artificial Intelligence at IIT Delhi.
- Experience: Brings over 25 years of rich teaching experience in Statistics and Computer Science.
- Expertise: As the coordinator of the IIT PAL Channel for Mathematics, he is dedicated to high-quality educational outreach.
Prof. Chatterjee's deep academic and practical insights ensure that complex concepts are delivered with clarity and relevance.
Who Should Take This Course?
This course is strategically designed to cater to a wide audience, making advanced statistical concepts accessible and applicable.
- Students (UG/PG): Students of Statistics, Mathematics, Economics, and Engineering seeking a strong theoretical and practical foundation.
- Practitioners: Professionals in data analysis, research, and fields requiring data-driven decision-making.
- Aspiring ML Engineers: Individuals aiming to work in Machine Learning and AI, as statistical inference is a critical prerequisite for understanding algorithms and model evaluation.
What You Will Learn: Course Layout & Weekly Breakdown
The 8-week curriculum is structured to build your knowledge from the ground up, seamlessly connecting probability theory to advanced inferential techniques.
| Week | Topics Covered |
|---|---|
| Week 1 | Revision of Probability, Different Discrete and Continuous Distributions |
| Week 2 | Functions of Random Variables, T, Chi-sq, F distributions and their Moments |
| Week 3 | Introduction to Statistics, Distinction between Data & Probabilistic Models |
| Week 4 | Estimators & Methods of Estimation, Properties: Consistency, Unbiasedness, Efficiency, Sufficiency |
| Week 5 | Neyman Factorization Theorem, Cramer-Rao Bound |
| Week 6 | Confidence Intervals, Concepts of Hypothesis Testing, Types of Errors |
| Week 7 | Inference on Population Mean/Variance, Comparing Two Populations |
| Week 8 | Neyman-Pearson Lemma |
Course Prerequisites & Industry Relevance
To ensure you get the most out of this course, a background in basic Probability and fundamental knowledge of data collection and descriptive statistics is recommended.
Industry Support: The skills taught—particularly Parameter Estimation and Testing of Hypothesis—are fundamental requirements across industries like pharmaceuticals (clinical trials), finance (risk modeling), market research, technology (A/B testing), and any field that relies on data analysis to validate assumptions and guide strategy.
Recommended Textbooks
Supplement your learning with these authoritative texts, as suggested by the instructor:
- Probability and Statistics for Engineers and Scientists (4th Ed.) by Anthony J. Hayter
- Statistical Methods (3rd Ed.) by R.J. Freund, W.J. Wilson, and D.L. Mohr
- Mathematical Statistics: A Textbook by S. Biswas and G.L. Sriwastav
Why This Course is Essential for Your Growth
This course goes beyond formulas. It provides a deep conceptual understanding of how we reason from samples to populations. By blending theoretical foundations with practical problem-solving, it equips you not just to perform tests, but to understand their logic, assumptions, and limitations. For anyone serious about a career in data science, analytics, or research, this knowledge is the differentiator between a technician and a scientist.
Embark on this 8-week journey with Prof. Niladri Chatterjee to build an unshakable foundation in Statistical Inference—the language of uncertainty and the engine of insight in the data age.
Enroll Now →