Data science, statistical consulting, graphics and visualization, industrial statistics, and experimental design
My current interests focus on developing best practices for data science and statistical consulting. I view best practices as a set of organizational practices that not only include coding, but also the thought processes and resulting analysis strategies. So in addition to advising students on how to organize their code, I also provide a framework for approaching complex problems. In general, I find that these skills need to be learned through practical experience and mentoring.
Some particular topics of interest to me are the use of GitHub for version control, the organization of folders and files, including standard naming conventions, best practices for organizing code, best practices for writing code, and best practices for analysis strategies. I generally recommend the use of the R software language for most applications, but the practices that I advocate apply equally well to Python and other languages.