[cycomanic]

I think you are seriously overestimating the "programming proficiency" of many scientists. I don't think the OP meant that scientists should be experts in the web stack or even in the intricate details of the scientific stack. However, I do expect that they should know how to write reasonable maintainable code, i.e. use functions, modules, don't just copy paste code around between cells etc.. (this is seriously the state of much of the scientific programming world).

>The quantity of information required to be learned is of one or two orders of magnitude, because the field of maths required to perform physics is quite stable, and well understood.

Apart from the fact that some areas of physics are really at the forefront of maths, this also ignores the fact that learning the level of proficiency required for graduate work in physics is significantly more involved than learning about some best practices in programming.

[BiteCode_dev]

If you think functions and modules as an example of what makes a code maintanable, then I'm afraid we won't be able to agree.

I've seen data scientists handling big code bases. The problem was not they couldn't use the language features. The problem is that they would be always lacking essential information for their mission because their is not enough time in a day for a regular human being.

They would put a md5 hashed password in their db, create an xml format to be reusable only to realize they'll need to hard code some value later, or have a gunicorn running to a crawl because they didn't know how to calibrate the number of workers.

It's just too many things to know. Once they mastered that, other things would come to bite them.

[mountainriver]

> If you think functions and modules as an example of what makes a code maintanable,

It’s definitely a part of it. This isn’t an all or nothing thing, one can learn good practices without encumbering their scientific work.