Data Analytics is the science of analyzing data to convert information to useful knowledge. This knowledge could help us understand our world better, and in many contexts enable us to make better decisions. While this is the broad and grand objective, the last 20 years has seen steeply decreasing costs to gather, store, and process data, creating an even stronger motivation for the use of empirical approaches to problem solving. This course seeks to present you with a wide range of data analytic techniques and is structured around the broad contours of the different types of data analytics, namely, descriptive, inferential, predictive, and prescriptive analytics.
This course is offered jointly by Prof. Nandan Sudarsanam. The tables below enlists the courses materials for Week 1 to Week 9. Each topic has both YouTube link and VideoKen link.
Week 1  Course Overview & Descriptive Statistics
Course overview  1 


Course overview  2 


Descriptive statistics  graphical approaches 


Descriptive statistics  measures of central tendency 


Descriptive statistics  measures of dispersion 


Assignment 1 
Solution 1 
Week 2  Probability Distributions & Inferential Statistics
Random variables & probability distributions 


Probability distributions  2 


Probability distributions  3 


Inferential statistics  motivation 


Inferential statistics  single sample tests 


Assignment 2 
Solution 2 
Week 3  Inferential Statistics
Two sample tests 


Type I & type II errors 


Confidence intervals 


ANOVA and test of independence 


Short introduction to regression 


Assignment 3 
Solution 3 
Week 4  Machine Learning
Introduction to machine learning 


Supervised learning 


Unsupervised learning 


Ordinary least squares regression 


Simple & multiple regression in Excel & Matlab 


Regularisation/coefficient shrinkage 


Data modelling & algorithmic modelling approaches 


Assignment 4 
Assignment 4 data 
Solution 4 
Week 5  Supervised Learning (Regression & Classification Techniques) I
Logistic regression 


Training a logistic regression classifier 


Classification & regression trees  1 


Classification & regression trees  2 


Bias variance dichotomy 


Model assessment and selection 


Support vector machines  1 


Support vector machines  2 


SVMs for nonlinearly separable data 


SVMs & kernel transformations 


Assignment 5 
Assignment 5 data 
Solution 5 
Week 6  Supervised Learning (Regression & Classification Techniques) II
Ensemble methods & random forests 


Artificial neural networks  1 


Artificial neural networks  2 


Deep learning 


Assignment 6 
Solution 6 
Week 7 Association Rule Mining & Big Data
Association rule mining  1 


Association rule mining  2 


Big data  a small introduction  1 


Big data  a small introduction  2 


Assignment 7 
Solution 7 
Week 8  Clustering Analysis & Predictive Analytics
Clustering analysis  1 


Clustering analysis  2 


Introduction to experimentation & active learning  1 


Introduction to experimentation & active learning  2 


Introduction to online learning  reinforcement learning  1 


Introduction to online learning  reinforcement learning  2 


Assignment 8 
Solution 8 
Week 9  Course Summary
Course summary & insights into the final exam