I keep asking myself why I should learn python (but doing it anyway)

Only bother with Python if you actually need it and plan to use it, waste of time otherwise, since knowing C and one or two other languages already allows you to understand Python code if you ever need to, so again, only put time into it if you actually plan to use it, otherwise learn some other language instead that will be more useful in life (eg: Pascal or something that is specialized)... I had to learn Python in for Uni, literally haven't needed to use it since even once, so was basically wasted time, even D has seen more use out of me compared to Python.

I have found that being lectured on stuff I have long since mastered to be tedious, and then you try to skip it but in there is peppered a few key concepts so I get to learn once again about hexadecimal and ASCII. There are a few books out there for various things which do appreciated some people occasionally want to cross skill but I am not sure what I have for python (for C I usually used Apress' Beginning C from novice to professional, said series does include a python one though I did not read that, the others I did skim in the series however followed form). If you want to go looking then you might find something.

To answer the question. Data science is quite literally the bridge between science, computer science and business for a lot of things. As you say nobody other than data scientists, statisticians and maybe a select handful of engineers with a stats focus knows R*. If you then speak python you can speak to general science, general computing (to say nothing of python being used as a database bridge all the time) and increasingly even some business types (or at least someone in their department).

*I did hear of some astronomy types doing something but they tend to get sucked back into pascal and whatever other legacy pieces of code still permeate that field.

@ThoD I read that machine learning is better in python than R, so this is more like a FOMO for me. I bought another course on machine learning, and it introduced both R and python, and I got this pressure to learn python before continue digesting this course. well my academic background is political science, but somehow I ended up as a data analyst and here I am, trying to catch up with this data science stuff.

Did it mention why machine learning may or may not be better?

I can't say I have investigated R as a machine learning... platform would be the wrong word but I will use it in a normal person sense there. As R is said stats and data science set then you have a limited pool vs everybody else which probably can muddle through with python. To that end I am expecting libraries, existing programs, updates to both of those, prototypes and more to be far more readily available in it.
As a general concept though both are pretty high level languages and while R might have a few more leanings into some of the data types that work here then python by virtue of its database stuff probably still works here. If speed becomes an issue (and if you are running billions of tests/simulations then it will be before long) then or someone which can do performance will play harder and go further -- see all those high frequency trading peeps learning C and whatever to program their FPGAs
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Python has seen wide adoption is most data science fields, esp. ML. I would say it is the de facto language of ML at this point. If you want to do any research or (more likely) plug and play other people's models, learning Python, Python virtual environments, and (probably) docker are all a must.

As far as learning new programming languages, you hit the nail on the head: once you understand the core concepts behind a handful of general purpose languages (C and C++ serve a as a good foundation for pretty much everything), there is no need to go through a dedicated course for learning any additional ones. The concepts are maintained from one language to the next, so really the only thing you need to do is find a cheat sheet for the syntax of any new ones.

PityOnU said:

The concepts are maintained from one language to the next, so really the only thing you need to do is find a cheat sheet for the syntax of any new ones.

Click to expand...

Is this how we get the "everybody programs well in their first language" lines of thought?

That said while it is probably no so bad for R to python or vice versa then hit up something low level like C that will not hold your hand that much when it comes to data types, will allow you to leak all your memory away, will have some deceptively named and... I am guessing why this is why we can have all the nice exploits.