Introduction to R: Basics and R-Language essentials; The Built-in distributions in R; Descriptive
Statistics and Graphics; One and two-sample tests; Regression and Correlation; Anova; Computation of
Sample Size; Logistic Regression; Programming in R. Introduction to data mining and statistical
learning. Data Preprocessing. Classification Methods: Classification and Regression Tree, Bagging and
Random Forests, Neural Network. Clustering Methods: Hierarchical and k-Means Clustering.
Association: Market Basket Analysis. Applications in R.
