Instructor:
Shulei Wang (shuleiw at illinois dot edu)
TA:
Linjun Huang (linjunh2 at illinois dot edu)
Course Website: Canvas
Office Hours:
Tuesday 3:30-4:30 pm and Thursday 3:00-4:00 pm (CST) by Linjun Huang
Monday 9:00-10:00 am (CST) by Shulei Wang
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 simulation experiments, Monte Carlo methods, resampling methods, statistical model fitting, large data set manipulation, and basic data visualization. This course will strike 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.
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)
R and R Markdown
Advanced R
Plotting in R
Random variable generation
Monte Carlo integration
Simulation
Bootstrap and Jackknife
Permutation test
Markov Chain Monte Carlo
R and RStudio (preferred) will be used and all are free to download. (Getting started with R and RStudio)
Homework Assignments (30%)
Take-home Midterm Exams (40%)
Final Project (30%)