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Any tips to help a scientist become a better programmer? (iusearchlinux.fyi)

submitted 8 months ago by [email protected] to c/[email protected]

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Hey there!

I'm a chemical physicist who has been using python (as well as matlab and R) for a lot of different tasks over the last ~10 years, mostly for data analysis but also to automate certain tasks. I am almost completely self-taught, and though I have gotten help and tips from professors throughout the completion of my degrees, I have never really been educated in best practices when it comes to coding.

I have some friends who work as developers but have a similar academic background as I do, and through them I have become painfully aware of how bad my code is. When I write code, it simply needs to do the thing, conventions be damned. I do try to read up on the "right" way to do things, but the holes in my knowledge become pretty apparent pretty quickly.

For example, I have never written a class and I wouldn't know why or where to start (something to do with the init method, right?). I mostly just write functions and scripts that perform the tasks that I need, plus some work with jupyter notebooks from time to time. I only recently got started with git and uploading my projects to github, just as a way to try to teach myself the workflow.

So, I would like to learn to be better. Can anyone recommend good resources for learning programming, but perhaps that are aimed at people who already know a language? It'd be nice to find a guide that assumes you already know more than a beginner. Any help would be appreciated.

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[–] [email protected] 2 points 8 months ago

The reason they don't do multi people and multi year coding projects has nothing to do with repeatability of the experiments, most science coding is done through simple-ish code that uses existing libraries, doesn't code them. That code is usually stored in notebooks (jupyter, zeppelin) or simple scripts.

For science code, it usually falls in the realm of data analysis, and as a data engineer, let me tell you that the analysis part of the job is usually very ad-hoc modifications of the script and live coding though notebooks and such.

The part where whatever conclusion of the research is then transformed into a functioning application, taking care of naming conventions, the architecture of the system where the input data, the transformations, the postprocessing and such is done, is usually done by another team of dedicated data engineers or software developers.

I guess that it would be helpful for the analysis part to have standardized templates for data extraction and such, but usually the tools used in the research portion of the process and the implementation portion are completely different (python with tensorflow vs C++ with openvino or whatever cloud based) so it's not really fair to load the architecture design since the beginning.