I received an interesting email yesterday from a grad student who took this course the last time it was offered at the University of Toronto. It said in part:
My supervisor could better advise students doing computational work if they had more background knowledge. They are routinely faced with questions like:
- Is a project possible, given the background of the student and the difficulty of the tasks?
- How long should a project take, and what can be considered good progress?
- What training should a student have?
- How to manage collaboration between students, data archives, etc?
- How to make sense of and build upon work done by previous students?
On a more personal note—I would enjoy my supervisor having a clearer idea of what I do.
It's an interesting list, and quite different from a grad student's. What else do you think people directing computational research, rather than doing it themselves, need to know?
Originally posted at Software Carpentry.