Back to Courses

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