This repository contains homeworks and coding projects related to Foundations of Data Science course (Fall 2018). All the code for the projects is written in R programming langugage
Random variables and probability distributions, exploratory data analysis, variable selection, sampling methods, histograms and probability distributions, density estimation, missing data and imputation, mixture models, latent variables, and expectation maximization, regression analysis, discriminant analysis, bagging and boosting, principle component analysis, information theory -- entropy, mutual information, Bayesian information criteria, conditional independence, rescaling and low-dimensional summaries, factor analysis, graphical causal models and causal inference, and evaluating predictive models