[toddm]

Most of the replies to your query have addressed the big issue, which is that "data scientists" are almost universally mediocre (at best) coders. It's endemic to that job position and is guaranteed to be language-invariant.

One factor that isn't helping generations younger than mine (mid-50's) is the continual evolution of tools that remove the user from all the underlying parts. I recently worked with someone who told me they "only know Databricks on Azure" and "don't know python." Their self-assessment was accurate, and the utility of that individual was essentially zero.

The problem with python is that people like myself - non-engineers, and mostly end users of software - spend an inordinate amount of time dealing with mismatched library dependencies, deprecated features, rolling-back python versions to get a working kernel and so on.

The fact that the business model of at least two companies (Enthought and Anaconda) is predicated on the difficulty of getting a functioning python environment to work in this day and age speaks volumes about the problem.

If we can't get past "which pip?," how can we expect the other stuff to "just work?"

[ensan]

If you’re just an end user and a non-engineer, how can you (1) universally judge the level of programming of data scientists to be (2) mediocre?