Generally speaking most data science stuff appears to mostly be Python with a little R and then a very very small amount of many other ecosystems (e.g. most of

Generally speaking most data science stuff appears to mostly be Python with a little R and then a very very small amount of many other ecosystems (e.g. most of the elite banks do a lot of kdb+ in addition to a lot of Python). In engineering fields, it all just depends. Control systems (via Simulink) is still pretty rooted in Matlab last I checked and is a lot better documented than Python. In power systems (my field) most "programming" is either at the application level (like C, C++, or Fortran) if you're a vendor and at the scripting/API level with 95% of that being in Python and the other 5% probably being something like C#, Java, or shell script. All data analysis is either in Python or SQL. SQL is better if the data is already in a database. We deal with such large quantities of data that pandas usage is relatively rare though. It's more common to use a Python script to push a bunch of data to a SQL database to then do analysis. I do know some places that do post jobs with Matlab as a requirement and those are mostly the little R&D sections of those companies that tend to hire workers from academia that haven't been exposed to Python as much as Matlab. Comparing the two language ecosystems (what matters), I'd say that Python is much easier to do general programming in and ML, but Matlab has easier and definitely more consistent facilities for charting and graphs. I prefer Python for a lot of scripting work, but Matlab is still pretty nice if your company can afford a license. I wouldn't want to just be stuck in Matlab, but there are plenty of cases where I might reach for it over Python if available. At the end of the day though, they're both scripting languages and aren't very hard to learn. Any sane hiring manager should accept candidates with experience in either one as long as they don't need to hire a super expert to lead a big effort on day one.