The skills of ‘working on an existing project’ I mentioned above are not usually covered as part of a CS education, but complementary skills for most things you might want to do once you have one. I also agree entirely with gjm; you’ll learn a lot any time you get hands-on practice with close feedback from a mentor.
For OSS libraries, those pytest issues would be a great start. Scientific computing varies substantially by domain—largely with the associated data structures, being some combination of large arrays, sequences, or graphs. Tools like Numpy, Scipy, Dask, Pandas, or Xarray are close to universal though, and their developers are also very friendly.
The skills of ‘working on an existing project’ I mentioned above are not usually covered as part of a CS education, but complementary skills for most things you might want to do once you have one. I also agree entirely with gjm; you’ll learn a lot any time you get hands-on practice with close feedback from a mentor.
For OSS libraries, those pytest issues would be a great start. Scientific computing varies substantially by domain—largely with the associated data structures, being some combination of large arrays, sequences, or graphs. Tools like Numpy, Scipy, Dask, Pandas, or Xarray are close to universal though, and their developers are also very friendly.