>>12054304I use R and Python mainly, although I have experience in other languages and can pick them back up real easy (java, javascript, matlab (lol), etc etc).
Python is the big one for a lot of "data science" companies. I love R because its all stats and I love stats, also super-easy to do data-curation and the graphics with ggplot2 >> anything in python
>any tips for me on how to improve my coding and git gud?Biggest thing is you NEED a project. Learning to code without a project is like learning spanish without someone to talk to. Without the need for your mind to invent things/figure out things/having a goal, all you'll do is learn how to make functions and go "okay now what"
It also depends on what you want to do. I'm going to assume data science/machine learning.
If you want a fun deep-end, Kaggle. Kaggle has a billion datasets and goals, and a billion notebook (mostly python) that do everything from basic to hard machine learning.
It's a bit like drinking water from a firehose, but it really gives a breadth of topics, and its cutting-edge machine learning problems (so you're not doing baby datasets and baby machine learning, you will be competing/implementing the same code to the same problems that professionals do).
Go to kaggle, click on notebooks, start up your favorite python IDE (I use pycharm), click on something interesting (I see "A Beginners guide to data science"), and start copying. It will take a bit, but once you start getting a feel for it, pick some datasets (lots available at kaggle), lookup scikit examples, and start playing around. ML is really easy to code, and the steps aren't hard. The difficulty is in the intuition and creativity of how to treat one dataset from another.
