Section-I
Descriptive Statistics, Probability Distributions, Inferential Statistics through hypothesis tests, Regression, ANOVA (Analysis of Variance)
Section II
Differentiating algorithmic and model based frameworks, Regression: Ordinary Least Squares, Ridge Regression, Lasso Regression, K Nearest Neighbours Regression & Classification, Bias-Variance Dichotomy, Model Validation Approaches, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Regression and Classification Trees, Support Vector Machines
Section III
Ensemble Methods: Random Forest, Neural Networks, Deep learning, Clustering, Associative Rule Mining, Challenges for big data anlalytics, Creating data for analytics through designed experiments, Creating data for analytics through Active learning, Creating data for analytics through Reinforcement learning