Predictive Analytics Course: Regression & Classification | NPTEL

Course Details

Predictive Analytics: Your Gateway to Mastering Data Science

In today's data-driven world, the ability to predict future outcomes is not just an advantage; it's a necessity. Predictive models are the engines behind recommendation systems, financial forecasts, risk assessments, and countless other corporate applications. If you aspire to become a proficient Data Scientist, a deep understanding of these models is paramount.

This detailed blog outlines a premier 12-week postgraduate course on Predictive Analytics - Regression and Classification, offered on NPTEL and instructed by Prof. Sourish Das from the prestigious Chennai Mathematical Institute (CMI). This course is meticulously designed to build a strong statistical foundation for tackling real-world prediction problems.

Meet Your Instructor: Prof. Sourish Das

Prof. Sourish Das brings a rare blend of academic excellence and industry experience to the classroom. As a professor at CMI overseeing the MSc Data Science program, his expertise is grounded in a PhD in Statistics from the University of Connecticut, USA, and postdoctoral work at Duke University, UK. His industry experience and recognition, such as the Rutherford Fellowship, ensure the course content is both theoretically rigorous and practically relevant.

Who Should Take This Course?

This course is tailored for:

• M.Tech Computer Science or M.Sc Statistics students.
• Final-year B.Tech students with a solid background in Probability and Statistics.
• Aspiring Data Scientists looking to solidify their understanding of core predictive modeling techniques.

Prerequisite: A prior full course in Probability and Statistics is essential. The course is supported by industries like Banking, Financial Services, Manufacturing, Automotive, and ITES, highlighting its direct career relevance.

Detailed 12-Week Course Curriculum

The course is structured to take you from foundational concepts to advanced applications, with hands-on implementation.

Weeks 1-4: Foundations of Regression

The journey begins with the bedrock of predictive modeling: Linear Regression.

• Weeks 1 & 2: Introduction to least squares method, normal equations, and the Gauss-Markov theorem. Understand the assumptions behind reliable models and the metrics like Mean Squared Error (MSE) that gauge their performance.
• Week 3: Dive into the geometry of regression and the crucial art of Feature Engineering (or basis expansion) to model non-linear relationships. Learn to perform statistical inference on regression coefficients.
• Week 4: Master the diagnostics: learn to check model assumptions (independence, homogeneity, normality) using statistical tests like Breusch-Pagan and Kolmogorov-Smirnov. Compare models using R-squared, RMSE, AIC, and BIC.

Weeks 5-8: Advanced Regression & Applications

As models grow complex, new challenges and solutions emerge.

• Week 5: Grapple with the fundamental Bias-Variance Tradeoff and learn feature selection techniques to build parsimonious models.
• Week 6: Tackle Multicollinearity using Variance Inflation Factor (VIF) and solve ill-posed problems with Regularization techniques (LASSO, Ridge, Elastic Net).
• Week 7: Get hands-on! Implement regression analysis in Python, R, and Julia, the essential tools of a modern data scientist.
• Week 8: Explore powerful applications: the Capital Asset Pricing Model (CAPM) in finance, Bootstrap Regression for robust inference, Time Series Forecasting, and an introduction to Granger Causality.

Weeks 9-12: Mastering Classification

The focus shifts from predicting continuous values to classifying categories.

• Weeks 9 & 10: Introduction to Logistic Regression for binary classification. Learn estimation via Maximum Likelihood Estimation (MLE) and implement it in R, Python, and Julia.
• Week 11: Perform statistical inference on logistic models and explore alternative methods like Linear and Quadratic Discriminant Analysis (LDA & QDA).
• Week 12: Extend your knowledge to Generalized Linear Models (GLMs), covering Multinomial Logistic Regression for multi-class problems, Poisson Regression, and Negative Binomial Regression for count data.

Essential Course Textbooks

Why Enroll in This Predictive Analytics Course?

This course is more than a series of lectures; it's a comprehensive blueprint for building a career in data science. By blending theoretical depth (from Gauss-Markov to GLMs) with practical implementation across multiple programming languages and real-world applications (Finance, Forecasting), it equips you with the end-to-end skills demanded by the industry. Under the guidance of Prof. Sourish Das, you will not just learn algorithms but develop the statistical intuition to wield them effectively.

Take the next step in your data science journey. Master the predictive models that are shaping the future of business and technology.

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