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I'm currently split between Python and Julia, having used R happily in the past for data analysis and Matlab for this and that in my EE program. For me, Julia crushes one niche that the rest of them are not good at: making the math look like the math. https://docs.sciml.ai/ModelingToolkit/stable/tutorials/nonli... If you've used something like SciPy or symbolic Matlab or Maxima or whatever, it always feels like I'm v…
Julia is fun, but is still mostly an academic language. Very few shops will use it in the private sector. Python is also more common as a prototype integration language, and rarely seen in industrial areas. If you are an EE that wants to remain employed... than make sure you have documented hours with C/C++, Verilog on Zynq, and ladder logic for Rockwell automation products. Best of luck =3
I started using python for various engineering analysis problems around 2001 and I loved it for how fast (due to minimal boilerplate and automatic memory management) I could code up some thought relative to using C or Java. I could tackle problems in ways I just wouldn't have tried otherwise because I couldn't afford the longer time to write it in other languages. However, for problems which needed speed, of course it bogged down.
I started using Julia for ODE stuff in 2018 or 2019 and was thrilled with the speed and conciseness. As others have said, it looks much more like math and a lot of better design choices were made.
Python obviously has a much larger ecosystem and probably always will, and it will remain a safe choice, but you don't set yourself apart by doing the same thing as everyone else.