Computing Skills, Nunchaku Skills, Bow Skills…

I have been thinking for quite some time about the computing skills that graduate students will need as they exit our program. It is absolutely clear to me (not necessarily all of my colleagues) that students need computing skills. First, a little background…

I teach in the Quantitative Methods in Education program within the Educational Psychology Department at the University of Minnesota. After graduating, many of our students take either academic jobs, a job working in testing companies (e.g., Pearson, College Board, etc.), or consulting gigs.

I have been at conferences, read blogs, and papers in which the suggestions of students learning computing skills have been posited. I am convinced of this need at 100.4%, 95%CI = [100.3%, 100.5%].

The more practical issue is what computing skills should these students learn and how deeply? And, how should they learn them (e.g., in a class, on their own, as part of an independent study)?

The latter question is important enough to merit its own post (later), so I will not address that here. Below I will begin a list of the computing skills that I believe the Quantitative Methods students should learn, and I hope readers will add to it. I use the word computing rather broadly as a matter of intention. I also do not list these in any particular order at this point, other than how they come to mind.

  • At least on programming language (probably R)
    • In my mind two or three would be better depending on the content focus of the student (Python, C++, Perl)
  • LaTeX
  • Knitr/Sweave (I used to say Sweave, but Knitr is easier initially)
  • HTML/HTML5
  • CSS
  • KML
    • I think students should also know about PHP and¬†Javascript. Perhaps they don’t have to be fluent in them, but they are important to know about. For example, to learn D3 (a visualization toolkit) it would behoove a student to learn Javascript.
  • Markdown/R Markdown. These are again, easy to learn and could help students transition to easily learning Knitr. It could also lead to learning and using Slidify.
  • Regular Expressions
  • SQL
  • XML
  • JSON
  • XPATH
  • BibTeX (or some program to work with references….Mendeley, EndNote, something…)
  • Some other statistical programs. Some general (e.g., SAS, SPSS); some specific (MPLUS, LISREL, OpenMX, AMOS, ConQuest, WinSteps, BUGS, ¬†etc.)
  • Unix/Linux and Shell Scripting

I think students could learn many of these at a lesser level. The basics and using them to solve simpler problems. In this way there is at least exposure. Interested students could then take it upon themselves (with faculty encouragement) to learn more about specific computing skills that are important for their own research.

What have I missed?