[qsi]

Syntax is one thing, but as others have mentioned it's more than that. Libraries, tooling, IDE and perhaps also knowing the ideosyncracies and pitfalls, and how to recover from them.

I'm very comfortable in Matlab and often know immediately what's wrong when I hit those oddities. In Python it usually means I spend considerable time googling and tinkering before I even understand what I did wrong because I have far less experience... Same when I tinker with Julia.

And for some people it being a Real Programming Language may be a disadvantage actually... Typically means you need a better understanding than just type-and-run.

[wirrbel]

I can mirror your sentiment from the python side. I started my thesis in a matlab heavy department after years of python experience. After three months of fighting I switched to data processing in python and it was a breeze (even though Pandas was kind of an experimental library back then). The matlab ide would often crash when there were problems on the matlab code that my supervisor had written (probably it wasn’t well written but i was astonished that it would bring down the IDE).

I have made a career in python data science since then overall it was a very good decision for me. I was ahead of the curve when data science popped up.

I have since joined a company with engineering dept that rely on matlab. I have no doubt that they wouldn’t get much benefit from switching to python or Julia apart from the license costs.