Graduate students in statistics often take (or at least have the opportunity to take) a statistical computing course, but often such courses are focused on methods (like numerical linear algebra, the EM algorithm, and MCMC) and not on actual coding.

For example, here’s a course in “advanced statistical computing” that I taught at Johns Hopkins back in 2001.

Many (perhaps most) good programmers learned to code outside of formal courses. But many statisticians are terrible programmers and would benefit by a formal course.

Moreover, applied statisticians spend the vast majority of their time interacting with a computer and would likely benefit from more formal presentations of how to do it well. And I think this sort of training is particularly important for ensuring that research is reproducible.

One really learns to code in private, struggling over problems, but I benefited enormously from a statistical computing course I took from Phil Spector at Berkeley.

Brian Caffo, Ingo Ruczinski, Roger Peng, Rafael Irizarry, and I developed a statistical programming course at Hopkins that (I think) really did the job.

I would like to develop a similar such course at Wisconsin: on statistical programming, in the most general sense.

I have in mind several basic principles:

• be self-sufficient
• get the right answer
• document what you did (so that you will understand what you did 6 months later)
• if primary data change, be able to re-run the analysis without a lot of work
• are your simulation results reproducible?
• reuse of code (others’ and your own) rather than starting from scratch every time
• make methods accessible to (and used by) others

Here are my current thoughts about the topics to include in such a course. The key aim would be to make students aware of the basic principles and issues: to give them a good base from which to learn on their own. Homework would include interesting and realistic programming assignments plus create a Sweave-type document and an R package.

• Basic unix tools (find; df; top; ps ux; grep); unix on Mac and windows
• Emacs/vim/other editors (rstudio/eclipse)
• Latex (for papers; for presentations)
• slides for talks; posters; figures/tables
• Advanced R (fancy data structures; functions; object-oriented stuff)
• Advanced R graphics
• R packages
• Sweave/asciidoc/knitr
• minimal Perl (or Python or Ruby); example of data manipulation
• Minimal C (or C++); examples of speed-up
• version control (eg git or mercurial); backups
• reproducible research ideas
• data management
• managing projects: data, analyses, results, papers
• programming style (readable, modular); general but not too general
• debugging/profiling/testing
• high-throughput computing; parallel computing; managing big jobs
• finding answers to questions: man pages; documentation; web
• more on visualization; dynamic graphics
• making a web page; html & css; simple cgi-type web forms?
• writing and managing email
• managing references to journal articles