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# Introduction to Data Analytics

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 non-linearly 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