Data Analysis Using Statistical Learning Techniques


This is an AICTE approved Short Term Program

SKU: Department of Mathematics | Start Date: 21-03-2022 | End Date: 26-03-2022 Categories: ,


This is an AICTE approved Short Term Program

Department: Department of Mathematics

Intended audience: Industry participants, College students, Faculty

About: The objective of the course is to teach the students a set of statistical tools to uncover and understand patterns in complex datasets. We are living in a world with an ever-growing collection of datasets and computational power. The digital world has equipped us with the storage of huge datasets. The advances in computational power have enabled us to apply the state-of-the-art developments in theoretical statistics on empirical datasets obtained from many scientific areas as well as marketing, finance, and other business disciplines. People with statistical learning skills are in high demand due to the ever-increasing role of data-driven decision making. At the end of the course, the students can be expected to get an intuitive and conceptual understanding of the statistical tools and enough expertise in programming languages such as R/Python to readily apply these statistical methods on different datasets.

The following is the syllabus of the course:

1: Course intro: Regression, classification, survival, unsupervised learning, empirical applications

2: General techniques: K-nearest neighbour, Bias-variance trade off, overfitting

3: Linear regression- Multiple linear regression, dummy variable, interactions, hypothesis testing

4: Linear models for classification- logistic regression, LDA, QDA, ROC curve

5: Resampling techniques: Cross validation, Bootstrap

6: Model selection: AIC, BIC, Regularisation (lasso +ridge), Stepwise regression

7: Tree-based methods: Trees, random forest, boosting

8: Bayesian inference: prior, posterior, map, regularisation in Bayesian setup, intro to mcmc

9: Unsupervised learning: PCA, k-means clustering, hierarchical clustering, Gaussian mixture model

10: Survival analysis: Kaplan Maier plot, Cox proportional hazard model, log rank test

11: Interactive session

12: Neural network

Session dates: 21-03-2022 to 26-03-2022

Time: 10 AM to 1 PM & 2:30 PM to 5:30 PM IST

Last date of registration: 18-03-2022


Profile of the Instructor(s)

Name: Jayant Jha

Profile: Assistant Professor, Department of Mathematics, IIT Madras. Assistant Professor with experience in working in the field of theoretical and applied statistics in academia as well as industry. Skilled in Statistical Modeling, R, Bayesian inference, and Machine learning. Strong education professional with a Doctor of Philosophy (Ph.D.) focused in Mathematical and Applied Statistics from Indian Statistical Institute, Kolkata.

Name: Neelesh S. Upadhye

Profile: Associate Professor, Department of Mathematics, IIT Madras. Experienced Associate Professor with a demonstrated history of working in the higher education industry. Skilled in Mathematical Modeling, R, Stochastic Modeling, and Statistical Modeling. Strong education professional with a Doctor of Philosophy (Ph.D.) focused in Mathematical Statistics and Probability from Indian Institute of Technology, Bombay.

Eligibility & Fees

Eligibility requirement of participants: Exposure to probability/statistics/data analysis

Maximum number of participants that can be accommodated: 

  • Faculty – 50
  • Industry participants – 50
  • Students – 50


  • Faculty – ₹1000
  • Industry participants – ₹5000
  • Student – ₹1000

“The registration fee will be refunded for the first 30 faculty participants from AICTE colleges once they attend and complete the training program”.

Click here to download the Sponsorship Certificate format.

Click here to submit your sponsorship certificate and other details.


Criteria: Attending all the sessions and submitting the assignments, if any.

Certificate template:


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