Data Science for Engineers Course | IIT Madras NPTEL | Learn R, Algorithms
Course Details
| Exam Registration | 5869 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 8 weeks |
| Categories | Computer Science and Engineering, Data Science, Programming |
| 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 |
Why Should Engineers Master Data Science?
In today's data-driven industrial landscape, the ability to extract insights from data is no longer a niche skill but a core engineering competency. From optimizing manufacturing processes and predicting equipment failures to enhancing product design and quality control, data science is revolutionizing how engineers solve problems. The Data Science for Engineers course, offered by the prestigious Indian Institute of Technology Madras (IIT Madras) on the NPTEL platform, is meticulously designed to bridge this critical skill gap. This 8-week program provides a rigorous, application-oriented foundation, empowering engineers to leverage data as a powerful tool in their domain.
Meet Your Distinguished Instructors
The course is led by two eminent professors from IIT Madras, bringing decades of research and industry-relevant expertise to the curriculum.
Prof. Ragunathan Rengasamy
Prior to joining IIT Madras, Prof. Rengasamy was a Professor of Chemical Engineering and Co-Director of the Process Control and Optimization Consortium at Texas Tech University, USA. He has also held faculty positions at Clarkson University and IIT Bombay. His primary research focuses on fault detection, diagnosis, and developing data science algorithms for manufacturing industries, ensuring the course content is grounded in real-world industrial challenges.
Prof. Shankar Narasimhan
Prof. Narasimhan is a Professor in the Department of Chemical Engineering at IIT Madras. His key research areas include data mining, process optimization, and fault detection and diagnosis. He is the co-author of the well-regarded book Data Reconciliation and Gross Error Detection: An Intelligent Use of Process Data, highlighting his deep expertise in the practical application of data analysis techniques in engineering.
Course Overview: Objectives & Outcomes
Learning Objectives
- Introduce R as a programming language for data analysis.
- Establish the mathematical foundations required for data science.
- Introduce first-level data science algorithms.
- Present a structured data analytics problem-solving framework.
- Implement learning through a practical capstone case study.
Intended Learning Outcomes
Upon completion, learners will be able to:
- Describe a flow process for solving data science problems.
- Classify data science problems into standard types.
- Develop R code for data science solutions.
- Correlate results with the chosen solution approach.
- Assess and evaluate the effectiveness of a solution approach.
- Construct use cases to validate and refine approaches.
Detailed 8-Week Course Layout
| Week | Topics Covered |
|---|---|
| Week 1 | Course philosophy and introduction to R programming. |
| Week 2 | Linear Algebra for Data Science: Algebraic view (vectors, matrices, rank, pseudo-inverse) and Geometric view (projections, eigenvalue decomposition). |
| Week 3 | Statistics: Descriptive statistics, probability, distributions, covariance matrix, normal distributions, hypothesis testing, confidence intervals. |
| Week 4 & 5 | Optimization techniques and the Typology of Data Science problems with a solution framework. |
| Week 6 | Linear Regression: Simple and multivariate linear regression, model assessment, variable importance, subset selection. |
| Week 7 | Classification using Logistic Regression. |
| Week 8 | Classification using k-Nearest Neighbors (kNN) and Clustering with k-means. |
Who Should Enroll and Prerequisites
INTENDED AUDIENCE: This course is ideal for undergraduate and postgraduate students in any engineering discipline (Chemical, Mechanical, Computer Science, etc.), practicing engineers in industry, and any interested learner looking to build a strong foundation in applied data science.
PREREQUISITES: To ensure all participants can keep pace, the course provides 10 hours of pre-course material. Learners are strongly encouraged to practice this material to be fully prepared for the start of the course. A basic understanding of mathematics is beneficial.
Industry Recognition and Support
The practical relevance of this course is underscored by its support from leading global and Indian industries, including: Honeywell, ABB, Ford, and Gyan Data Pvt. Ltd. This industry backing confirms that the skills taught are directly applicable to current technological and analytical challenges in the engineering sector.
Recommended Textbooks
- Introduction to Linear Algebra by Gilbert Strang
- Applied Statistics and Probability for Engineers by Douglas Montgomery
Conclusion: Transform Your Engineering Career with Data
The Data Science for Engineers course from IIT Madras is more than just an academic program; it's a career investment. By demystifying the mathematics, programming, and algorithms of data science within an engineering context, Professors Rengasamy and Narasimhan provide a unique and powerful learning pathway. Whether you aim to innovate in R&D, optimize plant operations, or build intelligent systems, this 8-week journey equips you with the critical thinking and technical skills to harness the power of data. Enroll today and take the first step towards becoming a data-savvy engineer for the future.
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