Engineering Statistics Course | IIT Bombay | Prof. Manjesh K. Hanawal
Course Details
| Exam Registration | 38 |
|---|---|
| Course Status | Ongoing |
| Course Type | Core |
| Language | English |
| Duration | 12 weeks |
| Categories | Multidisciplinary |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 26 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Engineering Statistics: Your Foundational Guide to Data Analysis and Machine Learning
In today's data-driven world, the ability to systematically analyze and interpret information is a superpower. For engineers and aspiring data scientists, a solid grasp of statistics is not just an advantage—it's a necessity. This is where the Engineering Statistics course, offered by the prestigious Indian Institute of Technology Bombay (IIT Bombay), becomes an invaluable resource. Designed and taught by Prof. Manjesh Kumar Hanawal, this 12-week program is a masterclass in applying statistical principles to solve real-world engineering problems.
Meet Your Instructor: Prof. Manjesh K. Hanawal
Learning from an expert with both academic excellence and research prowess makes all the difference. Prof. Hanawal brings a wealth of knowledge to this course:
- Academic Credentials: M.S. in ECE from IISc Bangalore and a Ph.D. from INRIA & University of Avignon, France.
- Professional Journey: Postdoctoral research at Boston University before joining IIT Bombay as an Assistant Professor in Industrial Engineering and Operations Research.
- Research Expertise: His work spans performance evaluation, machine learning, and network economics.
- Recognitions: A recipient of the DST Inspire Faculty Award and the SERB Early Career Research Award, underscoring his contribution to the field.
Course Overview: What Will You Learn?
This multidisciplinary course is tailored for Undergraduate and Postgraduate students. It starts with core concepts and progressively integrates them with modern computational tools.
ABOUT THE COURSE: We are surrounded by data. This course provides the systematic framework needed to analyze it, extract meaningful insights, and build robust engineering solutions. A sound knowledge of statistics is the bedrock of developing effective machine learning and artificial intelligence algorithms. A key highlight is the hands-on exposure to statistical implementation using Python, a leading programming language in data science.
PREREQUISITES: A basic understanding of probability is required to embark on this journey.
INDUSTRY SUPPORT: As a fundamental course in data analysis, it holds recognition across industries that rely on data-informed decision-making.
Detailed 12-Week Course Layout
The curriculum is meticulously structured to build your knowledge from the ground up.
| Week | Topics Covered |
|---|---|
| Week 1-2 | Revising Probability: Axioms, Conditional Probability, Bayes’ Theorem, Random Variables, Distributions (CDF/PDF), Joint Distributions, Correlation, Limit Theorems. |
| Week 3 | Introduction to Python: Data visualization and fitting data to distributions. |
| Week 4 | Exponential Family, Population & Sampling, Sample Mean/Variance, Sampling from Normal, t-distribution, F-distribution. |
| Week 5-6 | Random Sample Generation: Order Statistics, Direct/Indirect Methods, Accept-Reject, Metropolis-Hastings Algorithm. Implementation in Python. |
| Week 7 | Data Reduction: Sufficiency Principle, Sufficient Statistics, Factorization Theorem. |
| Week 8 | Point Estimators: MLE, Method of Moments, Bayes Method, Expectation Maximization (EM), Consistency. |
| Week 9 | Evaluating Estimators: Bias, Mean Squared Error, Cramer-Rao Inequality, Fisher Information. |
| Week 10 | Hypothesis Testing: Likelihood Ratio Test (LRT), Type-I & Type-II Errors, Test Evaluation. |
| Week 11 | Regression & Intervals: Confidence Intervals, Simple & Multivariate Linear Regression, Logistic Regression, Goodness of Fit. |
| Week 12 | Statistical Tests & Applications: p-test, Kolmogorov-Smirnov test, f-score. Applying tests on datasets using Python. |
Essential Reference Books
To deepen your understanding, the course references several authoritative texts:
- Engineering Statistics by Montgomery, Runger, & Hubele (Wiley)
- Mathematical Statistics with Applications by Wackerly, Mendenhall, & Scheaffer (Duxbury)
- Mathematical Statistics and Data Analysis by John A. Rice (Thomson)
- Statistical Inference by Casella & Berger (Thomson)
Why Enroll in This Engineering Statistics Course?
This course is more than a syllabus; it's a career investment. It bridges the gap between theoretical statistics and practical, computational application—a skill highly sought after in fields like data science, AI/ML research, quantitative analysis, and industrial engineering. By combining Prof. Hanawal's expert instruction with hands-on Python labs, you will finish with the confidence to tackle complex data challenges and lay a formidable foundation for advanced study in machine learning.
Duration: 12 Weeks | Level: Undergraduate/Postgraduate | Category: Multidisciplinary
Take the first step towards mastering the language of data. Enroll in the Engineering Statistics course and transform how you see, analyze, and leverage information in the engineering world.
Enroll Now →