Creation of reproducible reports using R Markdown, visualization of data using ggplot2, importation, manipulation and summarization of data in R, use of data wrangling and data cleaning packages for R, writing of R functions that involve iterations or conditional statements, working with data that consists of dates, times, and strings.

Various approaches to statistical learning including empirical processes, classification and clustering, nonparametric density estimation and regression, model selection and adaptive procedures, bootstrapping and cross-validation.

Neural networks, nearest neighbor procedures, Vapnik Chervonenkis dimension, support vector machines, structural risk minimization induction, regularization methods and boosting and bagging in classification and regression.

Integration of knowledge at an advanced level, a review of recent developments and models in data science, an exploration of data ethics along with research and oral presentation.