this post was submitted on 15 Sep 2023
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For me the rule that has always worked is "bet everything on open-source". It has always paid off.
When people at uni used Matlab, I learned R (before R-studio even existed) and python. I moved to linux as soon as I could. I never wanted to learn anything MS or Apple specific, or proprietary technologies such as visual studio, excel, vba, c#, SAS. I went on docker ASAP...
Now the world in my field runs on open source tecnologies, and I am the leaders of the "new stuff" wherever company I go.
On the long term learning open source solutions is always a win. Best case scenario it becomes the industry standard, worst case scenario it gives you the know how to master proprietary tools
I'm curious why you chose R as an alternative to Matlab instead of Scilab. Scilab is specifically designed to be a free and open source alternative to Matlab.
For my thesis I was writing some test software and when deciding which language to use Matlab was immediately ruled out due to the cost (and also the extra cost for the toolkits I'd need). I instead went with Scilab which now means that anybody wanting to reproduce my results can do so freely.
Because at the time I needed more the statistical and plotting part. Ggplot was not yet a thing, but R was already pretty nice for plotting and stat.
I was using other, lower-level languages for more intensive tasks, as I was working in high performance computing.
Makes sense, thanks for the response! It is kind of fun to have a mix of the higher level (like R/Scilab) and lower level (which I used Fortran for mine).
We all started with fortran the old times in hpc and scientific computing. Kids nowadays don't know the thrill of retro engineering fortran code! /s