Data Analytics with Python


Prof. A. Ramesh

IIT Roorkee

*Additional GST and optional Exam fee are applicable.

SKU: IIT Roorkee Category:



We are looking forward to sharing many exciting stories and examples of analytics with all of you using python programming language. This course includes examples of analytics in a wide variety of industries, and we hope that students will learn how you can use analytics in their career and life. One of the most important aspects of this course is that you, the student, are getting hands-on experience creating analytics models; we, the course team, urge you to participate in the discussion forums and to use all the tools available to you while you are in the course!


Management, Industrial Engineering and Computer Science Engineering Students


Any analytics company


Ramesh Anbanandam graduated in Production Engineering from Madras University, Chennai. He did his post-graduation from National Institute of Technology, Trichy. He later earned his Ph.D. in Supply Chain Management from Indian Institute of Technology Delhi. His professional interest includes Humanitarian Supply Chain Management, Operations Management, Operations Research, Healthcare Waste Management, Sustainable Multi-model & Freight Transportation, Transportation Asset Management and Advanced Data Analytics using Python and R- programming. He has guided Ph.D. thesis in the area of Humanitarian Supply Chain Management, Healthcare waste management, and Reverse Logistics. He has published various research articles in reputed journals. He was also awarded Emerald Literati Award for Excellence under Highly Commended Research Paper in the Year 2011 and 2016 in the field of Supply Chain Management.

Additional information



Total hours


Certification Process

1. Join the course
Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 3000 + GST

2. Watch Videos+Submit Assignments
After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams
A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

Assignment score = Score more than 50% in at least 9/12 assignments.
Exam score = 50% of the proctored certification exam score out of 100
Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams
The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification
After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.


Course Details

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 : c2 Test and introduction to cluster analysis
Week 11 : Clustering analysis
Week 12 : Classification and Regression Trees (CART)

Books and References

1. McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. ” O’Reilly Media, Inc.”.
2. Swaroop, C. H. (2003). A Byte of Python. Python Tutorial.
3. Ken Black, sixth Editing. Business Statistics for Contemporary Decision Making. “John Wiley & Sons, Inc”.
4. Anderson Sweeney Williams (2011). Statistics for Business and Economics. “Cengage Learning”.
5. Douglas C. Montgomery, George C. Runger (2002). Applied Statistics & Probability for Engineering. “John Wiley & Sons, Inc”
6. Jay L. Devore (2011). Probability and Statistics for Engineering and the Sciences. “Cengage Learning”.
7. David W. Hosmer, Stanley Lemeshow (2000). Applied logistic regression (Wiley Series in probability and statistics). “Wiley-Interscience Publication”.
8. Jiawei Han and Micheline Kamber (2006). Data Mining: Concepts and Techniques. “
9. Leonard Kaufman, Peter J. Rousseeuw (1990). Finding Groups in Data: An Introduction to Cluster Analysis. “John Wiley & Sons, Inc”.


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