Course Overview

Computational data analysis is an essential part of modern statistics. This course is designed to help students develop programming skills and new computational tools for real data analysis. Through this course, students will learn the core ideas of statistically-oriented programming and how to debug and test their code. This course also covers the design of stochastic simulations experiment, Monte Carlo methods, resampling methods, statistical model fitting, large data sets manipulation and basic data visualization. This course will make a balance between programming skill training and real data analysis with examples. We will make use of the statistical computing software R in class and on homework assignments.  Extensive R programming background is not required.

Textbook

• The Art of R Programming: A Tour of Statistical Software Design, by Norman Matloff (Required)

• Statistical Computing with R, by Maria L. Rizzo (Required)

• R for Data Science, by Hadley Wickham & Garrett Grolemund (Required, link)

• The R Cookbook, by Paul Teetor (Optional, link)

• The R Graphics Cookbook, by Winston Chang (Optional, link)

• R Markdown Cookbook, by Yihui Xie, Christophe Dervieux & Emily Riederer (Optional, link)

• Advanced R, by Hadley Wickham (Optional, link)

• ggplot2: Elegant Graphics for Data Analysis, by Hadley Wickham (Optional, link)

• Fundamentals of Data Visualization, by Claus O. Wilke (Optional, link)

• R packages, by Hadley Wickham & Jenny Bryan (Optional, link)

Topic Outline

1. R and data structure

2. R Markdown, indexing, and iteration

3. Data frame and vectorization

4. Functions and objective-design

5. Testing and debugging

6. Plotting

7. Random variable generation

8. Monte Carlo integration

9. Simulation

10. Bootstrap and Jackknife

11. Permutation test

12. MCMC

13. Tidyverse

14. R package development

Software

• R and RStudio (preferred) will be used and all are free to download. (Getting started with R and RStudio)

Grading

• Bi-weekly homework assignments (50%): The lowest score will be dropped.

• Take-home Midterm (20%)

• Final project (30%)

More reading materials

• Quick-R

• R code for examples from the "Statistical Computing with R"

• Hands-On Programming with R

• Data Visualization with R