Python for Data Science Course | IIT Madras | Prof. Rengasamy
Course Details
| Exam Registration | 23390 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 4 weeks |
| Categories | Computer Science and Engineering, Artificial Intelligence, Data Science |
| Credit Points | 1 |
| Level | Undergraduate |
| Start Date | 19 Jan 2026 |
| End Date | 13 Feb 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 16 Feb 2026 |
| Exam Date | 28 Mar 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Python for Data Science: Your Gateway to Data-Driven Problem Solving
In today's data-centric world, the ability to extract insights and build intelligent solutions is a superpower. Python has emerged as the undisputed champion of data science, thanks to its simplicity, versatility, and powerful ecosystem of libraries. If you're a final-year undergraduate looking to master this essential skill, the Python for Data Science course from IIT Madras offers a structured and authoritative pathway.
This intensive 4-week program is designed to transform your theoretical knowledge of data science algorithms into practical, hands-on expertise using Python.
Learn from an Esteemed Instructor: Prof. Ragunathan Rengasamy
The course is led by Prof. Ragunathan Rengasamy, a distinguished academic with extensive international experience. Prior to joining IIT Madras, Prof. Rengasamy served as a professor and Co-Director of the Process Control and Optimization Consortium at Texas Tech University, USA. His career also includes professorships at Clarkson University and IIT Bombay. His major research in fault detection, diagnosis, and developing data science algorithms for manufacturing industries ensures the course content is grounded in real-world, industrial applications.
Who Is This Course For?
This course is meticulously designed for final-year undergraduate students in Computer Science, Engineering, Artificial Intelligence, and Data Science. A foundational knowledge of basic data science algorithms is a prerequisite, allowing the course to focus intensely on implementation and practical problem-solving in Python.
Course Overview: A 4-Week Learning Journey
The course is structured to take you from Python fundamentals to executing complete data science case studies. Here’s a detailed week-by-week breakdown:
Week 1: Building the Foundation
Get comfortable with the Python environment and core programming concepts.
- Basics of Python & Spyder (Tool): Introduction to the Spyder IDE, setting up your workspace, and managing files.
- Core Programming: Variable creation, arithmetic/logical operators, and understanding fundamental data types.
- Script Management: Learn to write, execute, comment, and debug your Python scripts effectively.
Week 2: Mastering Data Structures & NumPy
Dive deep into Python's data structures and the numerical computing powerhouse, NumPy.
- Sequence Data Types: In-depth operations on Strings, Lists, Tuples, Dictionaries, Sets, and Ranges.
- Introduction to NumPy: Understanding and manipulating the foundational ndArray for efficient numerical computations.
Week 3: Data Manipulation, Visualization & Control
Apply your skills to real data using Pandas and learn to create compelling visualizations.
- Pandas DataFrame: Perform data operations on the Toyota Corolla dataset, including reading files, exploratory data analysis (EDA), and preprocessing.
- Data Visualization: Use matplotlib and seaborn to create scatter plots, line plots, bar charts, histograms, box plots, and pair plots.
- Control Structures: Implement programming logic with if-else statements, for loops, while loops, break, and functions.
Week 4: Real-World Case Studies
Capstone your learning by solving two classic data science problems.
- Case Study 1: Regression – Build a model for Predicting the Price of Pre-Owned Cars.
- Case Study 2: Classification – Develop a model for Classifying Personal Income.
Recommended Reading & Resources
To supplement your learning, the course recommends these authoritative texts:
| Book Title | Author | Relevance |
|---|---|---|
| Introduction to Linear Algebra | Gilbert Strang | Strengthens the mathematical foundation for machine learning algorithms. |
| Applied Statistics and Probability for Engineers | Douglas Montgomery | Provides essential statistical knowledge for data analysis and inference. |
| Mastering Python for Data Science | Samir Madhavan | Offers practical Python-centric techniques and advanced data science concepts. |
Why Enroll in This Course?
This IIT Madras course provides a unique blend of academic rigor and practical application. You will not only learn Python syntax but also understand how to wield it to solve meaningful data science problems, guided by an instructor with profound industry and research experience. By the end of 4 weeks, you will have a strong portfolio of skills, from data wrangling and visualization to building predictive models, making you industry-ready for a career in data science, AI, and analytics.
Take the first step towards mastering data science with Python. Enroll today and unlock the power of data.
Enroll Now →