NPTEL : NOC:Introduction to Data Analytics (Management)

Co-ordinators : Dr. Nandan Sudarsanam, Dr. Balaraman Ravindran


Lecture 1 - Course Overview

Lecture 2 - Course Overview (Continued...)

Lecture 3 - Descriptive Statistics - Graphical Approaches

Lecture 4 - Descriptive Statistics - Measures of Central Tendency

Lecture 5 - Descriptive Statistics - Measures of Dispersion

Lecture 6 - Random Variables and Probability Distributions

Lecture 7 - Probability Distributions (Continued...)

Lecture 8 - Probability Distributions (Continued...)

Lecture 9 - Inferential Statistics - Motivation

Lecture 10 - Inferential Statistics - Single sample tests

Lecture 11 - Two Sample tests

Lecture 12 - Type 1 and Type 2 Errors

Lecture 13 - Confidence Intervals

Lecture 14 - ANOVA and Test of Independence

Lecture 15 - Short Introduction to Regression

Lecture 16 - Introduction to Machine Learning

Lecture 17 - Supervised Learning

Lecture 18 - Unsupervised Learning

Lecture 19 - Ordinary Least Squares Regression

Lecture 20 - Simple and Multiple Regression in Excel and Matlab

Lecture 21 - Regularization/ Coefficients Shrinkage

Lecture 22 - Data Modelling and Algorithmic Modelling Approaches

Lecture 23 - Logistic Regression

Lecture 24 - Training a Logistic Regression Classifier

Lecture 25 - Classification and Regression Trees

Lecture 26 - Classification and Regression Trees (Continued...)

Lecture 27 - Bias Variance Dichotomy

Lecture 28 - Model Assessment and Selection

Lecture 29 - Support Vector Machines

Lecture 30 - Support Vector Machines (Continued...)

Lecture 31 - Support Vector Machines for Non Linearly Separable Data

Lecture 32 - Support Vector Machines and Kernel Transformations

Lecture 33 - Ensemble Methods and Random Forests

Lecture 34 - Artificial Neural Networks

Lecture 35 - Artificial Neural Networks (Continued...)

Lecture 36 - Deep Learning

Lecture 37 - Associative Rule Mining

Lecture 38 - Association Rule Mining (Continued...)

Lecture 39 - Big Data - A small introduction

Lecture 40 - Big Data - A small introduction (Continued...)

Lecture 41 - Clustering Analysis

Lecture 42 - Clustering Analysis (Continued...)

Lecture 43 - Introduction to Experimentation and Active Learning

Lecture 44 - Introduction to Experimentation and Active Learning (Continued...)

Lecture 45 - An Introduction to Online Learning - Reinforcement Learning

Lecture 46 - An Introduction to Online Learning - Reinforcement Learning (Continued...)

Lecture 47 - Summary + Insights into the Final Exam