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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 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.

 

Week. No

Topics

1.

Descriptive Statistics
Introduction to the course
Descriptive Statistics
Probability Distributions

2.

Inferential Statistics
Inferential Statistics through hypothesis tests
Permutation & Randomization Test

3.

Regression & ANOVA
Regression
ANOVA(Analysis of Variance)

4.

Machine Learning: Introduction and Concepts
Differentiating algorithmic and model based frameworks
Regression : Ordinary Least Squares, Ridge Regression, Lasso Regression,
K Nearest Neighbours Regression & Classification

5.

Supervised Learning with Regression and Classification techniques -1
Bias-Variance Dichotomy
Model Validation Approaches
Logistic Regression
Linear Discriminant Analysis
Quadratic Discriminant Analysis
Regression and Classification Trees
Support Vector Machines

6.

Supervised Learning with Regression and Classification techniques -2
Ensemble Methods: Random Forest
Neural Networks
Deep learning

7.

Unsupervised Learning and Challenges for Big Data Analytics
Clustering
Associative Rule Mining
Challenges for big data anlalytics

8.

Prescriptive analytics
Creating data for analytics through designed experiments
Creating data for analytics through  Active learning
Creating data for analytics through Reinforcement learning

This course requires that you are familiar with high-school level linear algebra, and calculus. Knowledge of probability theory, statistics, and programming is desirable.


  1. Hastie, Trevor, et al. The elements of statistical learning. Vol. 2. No. 1. New York: springer, 2009.
  2. Montgomery, Douglas C., and George C. Runger. Applied statistics and probability for engineers. John Wiley & Sons, 2010


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