Data Analytics with Python Course | IIT Roorkee | Prof. A Ramesh
Course Details
| Exam Registration | 23185 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Computer Science and Engineering, Data Science |
| 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 | 19 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Unlock the Power of Data: A 12-Week Journey into Data Analytics with Python
In today's data-driven world, the ability to extract meaningful insights from raw information is a superpower. Whether you're in management, engineering, or computer science, mastering data analytics is no longer optional—it's essential. We are thrilled to introduce a comprehensive online course, Data Analytics with Python, designed to equip you with this critical skill set, guided by an expert from one of India's premier institutions.
Meet Your Instructor: Prof. A Ramesh from IIT Roorkee
Learning from the best ensures a strong foundation. This course is led by Prof. A Ramesh (Anbanandam), a distinguished academic and practitioner from the Indian Institute of Technology Roorkee.
Prof. Ramesh brings a wealth of knowledge and experience to the virtual classroom. He holds a Ph.D. in Supply Chain Management from IIT Delhi and has extensive research and teaching expertise in areas like Humanitarian Supply Chain Management, Operations Research, Healthcare Waste Management, and, crucially, Advanced Data Analytics using Python and R. An award-winning researcher (Emerald Literati Award 2011 & 2016), he has guided numerous Ph.D. scholars and published extensively in reputed international journals. His practical and academic insights will bridge the gap between theoretical concepts and real-world application.
Who Is This Course For?
This course is meticulously designed for a broad audience, making advanced analytics accessible to all.
- Intended Audience: Undergraduate and Postgraduate students in Management, Industrial Engineering, and Computer Science & Engineering.
- Industry Support: The curriculum is relevant for any analytics company, ensuring the skills you learn are industry-ready.
- Prerequisites: A basic knowledge of Python is beneficial but not mandatory. The course starts with Python fundamentals, making it beginner-friendly while progressing to advanced topics.
What Will You Learn? Course Layout & Modules
Spanning 12 intensive weeks, the course progresses from foundational statistics to advanced machine learning techniques, all implemented using Python.
| Week | Topic |
|---|---|
| Week 1 | Introduction to data analytics and Python fundamentals |
| Week 2 | Introduction to probability |
| Week 3 | Sampling and sampling distributions |
| Week 4 | Hypothesis testing |
| Week 5 | Two sample testing and introduction to ANOVA |
| Week 6 | Two way ANOVA and linear regression |
| Week 7 | Linear regression and multiple regression |
| Week 8 | Concepts of MLE and Logistic regression |
| Week 9 | ROC and Regression Analysis Model Building |
| Week 10 | Chi-Square Test and introduction to cluster analysis |
| Week 11 | Clustering analysis |
| Week 12 | Classification and Regression Trees (CART) |
Hands-On Learning and Industry Applications
This course emphasizes practical, hands-on experience. You won't just learn theory; you will build analytics models using Python. Through exciting stories and examples from various industries, Prof. Ramesh will demonstrate how analytics solves real-world problems. Students are encouraged to actively participate in discussion forums and utilize all available tools to maximize their learning. The goal is to show you how to leverage analytics in your career and daily life.
Recommended Reading & Resources
To complement the video lectures and assignments, the course references authoritative texts that form the backbone of modern data analytics:
- McKinney, W. (2012). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython.
- Black, K. Business Statistics for Contemporary Decision Making.
- Anderson, Sweeney & Williams. Statistics for Business and Economics.
- Montgomery & Runger. Applied Statistics & Probability for Engineering.
- Devore, J. Probability and Statistics for Engineering and the Sciences.
- Hosmer & Lemeshow. Applied Logistic Regression.
- Han & Kamber. Data Mining: Concepts and Techniques.
- Kaufman & Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis.
Why Enroll in This Data Analytics Course?
This course offers a unique blend of academic rigor from IIT Roorkee and practical relevance for the industry. Under the mentorship of an award-winning professor, you will build a robust portfolio of data analytics skills, from basic statistics to predictive modeling. In 12 weeks, you will transform from a beginner or intermediate learner into a confident practitioner capable of tackling data challenges with Python.
Take the first step towards mastering one of the most sought-after skills of the 21st century. Enroll today and begin your journey into the fascinating world of Data Analytics with Python.
Enroll Now →