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Another fun place to explore might be libraries for either language. Both have rich ecosystems of thousands of libraries that connect up to various things in the physical and digital world.
I've started my career with Python and after doing some Typescript for a while I've really fallen in love with Scala. The syntax in Scala 3 is very readable (after dealing with the initial discomfort of a new language) and the language feels, in same ways, like a natural evolution of Python. Having the possibility of using the whole Java ecosystem is also a huge plus. It's definitely not a perfect language but it sha…
Other comments point out that you should try to dig deeper into python. That's fair. But if you've only worked in a couple languages, learning a new language will give you more perspective on the languages you already know.
Learning any other language will probably be helpful, but Scala is an easy recommendation to make. It'll force you to learn new things, and it's a pleasant language to work in once you get used to it.
You could learn about basic anomaly detection systems or simple recommendation engines. At a simple level this is language agnostic although at a higher level you should use Python for machine learning. As for specific technologies possibly Rust which is like an OCaml and C hybrid. If you wish to think differently I recommend Haskell and Coq but these are not 'useful'.
Any recommendation of how to get started with recommendation engine and anomaly detection ? Should one grok all the math pre-reqs before starting with them, or can pick the math as we go along ? Appreciate your inputs.
Suppose your website has posts and you want to flag posts when they have abnormally high likes because they might be great reading or complimenting your new release. You could collect a dataset of likes after a day, X, of each post. Then calculate mean and variance, fit a normal distribution[1]. Then calculate z such that P(X >= z) = 0.01 (1%). z represents the cut off point at which typically only 1% of posts are above. Then when a post is above z say 1000 likes then you see what all the fuss is about.
I am just talking about applying 16-18 school maths in a simple way, to point out unlikely events. Of course the distribution of likes may not look like a normal curve if you plot (number of posts with x likes against x) so a different distribution may make more sense. It may not be a perfect model but just a quick and dirty thing to try, :).
Personally I enjoyed completing the free Andrew Ng Machine Learning course[2] on Coursera which covers this and quickly training a simple recommendation engine for movies. It also covers multi-variate Gaussian distributions if you want to flag based on more than one criteria. For this course, the maths is relatively accessible and they go over what you may have forgotten so you can pick up maths as you go along.
Of course you can go far more complex if you like but I don't know much about that.
[1] Normal distribution https://en.wikipedia.org/wiki/Normal_distribution
I'd say docker and sql. If you have some base that you want to build upon, extend the stack so that you are more versital and can handle issues around the stuff you are working on.