Assuming you're confident around CS fundamentals, good software engineering practices, and higher math (statistics, probability theory, linear algebra) — you can take shortcuts that won't cut it for complete newbies, like playing around with other people's Jupyter notebooks with handy examples and reading good books/papers that come with lots of prerequisites.
I'm curious to hear what do you want to learn Data Science for though. Stating your goal and possible areas you want to focus on would help to give you the right resources a lot.
Hi. Thanks a lot for your answer! Those links will be of great help. :)
I was actually skeptic about getting an answer on this after 24h without one - thought it would become unreachable.
I am indeed confident around higher math, analysis of algorithms, software engineering etc, maybe I'll get to take some shortcuts.
I work at a company that collects data from social platforms. We used to provide our clients a data visualization tool but, lately, we started to also analyse the data and build custom reports. The thing is none of us have studied data analysis (nor data science), and it is currently impossible for us to hire. So I realized how useful that kind of knowledge is, even if I'm not gonna learn it all in the short term.
You can navigate through some good books here: https://datasciencetexts.com/
Notebooks you can play with: https://github.com/WillKoehrsen/Data-Analysis (there's a link to Will's Medium page in the repo description, there is some good advice, too).
I'm curious to hear what do you want to learn Data Science for though. Stating your goal and possible areas you want to focus on would help to give you the right resources a lot.