Stat 428: Statistical Computing
Shulei Wang (shuleiw at illinois dot edu)
Yifan Chen (yifanc10 at illinois dot edu)
Xiang Cui (xiangc5 at illinois dot edu)
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.
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)
The R Cookbook, by Paul Teetor (Optional)
The R Graphics Cookbook, by Winston Chang (Optional)
R Markdown Cookbook, by Yihui Xie, Christophe Dervieux, Emily Riederer (Optional)
R and data structure
R Markdown, indexing and iteration
Data frame and vectorization
Functions and objective-design
Testing and debugging
Optimization and numerical methods
Read and write data in R
Fit the model
Tidyverse and versional control
Make own R package
Weekly quiz (20%): The lowest score will be dropped.
Homework assignments (30%): There are totally 4 assignments.
Final project (50%) : Presentation (20%) and report (30%)