Core Data Analysis

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Course Objectives

Module 1

Descriptive Statistics Introduction to the course Descriptive Statistics Probability Distributions

Module II

Inferential Statistics Inferential Statistics through hypothesis tests

Module III.

Regression & ANOVA Regression ANOVA(Analysis of Variance)

Module IV

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

Module V.

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

Module VI.

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

Module VII.

Unsupervised Learning and Challenges for Big Data Analytics Clustering Associative Rule Mining Challenges for big data analytics 8. Prescriptive analytics Creating data for analytics through designed experiments Creating data for analytics through Active learning Creating data for analytics through Reinforcement learning

Project & Exam