We're teaching a course at ETH Zurich [1] where --besides the actual payload of solving partial differential equations (PDEs) on GPUs-- we put a lot of emphasis on "tools". Thus students learn how to use git and submit their homework via pushing to a repo of theirs on github, we teach testing and continuous integration, writing documentation, running code on a cluster, etc. In their final project, again submitted as a GitHub repo, they need to make use of all of theses skills (and of course solve some PDEs).
Note that excellent work in this space is done by the Software Carpentry project which exists since 1998 [2].
As an alumni, thanks a lot for doing this. Looking back, all the things that I've learned in just the first few weeks in the industry made writing code so much more productive - if only someone had shown some of it already during some early semester, even just during some assistant teaching hour, it would have saved so many hours.
I remember specifically when one of the exercises for some compiler lecture contained unit tests the code had to satisfy, and I was like, wow, why didn't I already knew about this during algorithm classes earlier where I was fumbling around with some diff-tools to check my output. Let alone proper version control, now that would have been a blessing.
In hindsight, it's a bit embarrassing that I didn't bother to, well, just google for it, but neither did my colleagues - I guess we were so busy with exercises and preparing for exams that we just didn't have the time to think further than that.
Thank you very much for the GPU course. Even though my college taught shell usage to some extent, when I asked about GPU programming it was considered a nerd topic back in 2009.
Note that excellent work in this space is done by the Software Carpentry project which exists since 1998 [2].
[1] https://pde-on-gpu.vaw.ethz.ch/ [2] https://software-carpentry.org/