Quote:
Originally Posted by AKQJ10
I've used R and Python/numpy a bit for various academic research. I'd go with Python just because you're learning a real-life programming language that's useful for a lot more than just high-powered stats. R feels like it was written by academics, which is was, so its funky syntax tilts me. I did discover one really useful R function that wasn't present in a Python library, I forget which, but in general Python is fun and intuitive.
(R is valuable in the job market because it's mostly data science folks using it; Python is valuable because it's far far far more common in industry jobs.)
+1 for Anaconda -- comes with numpy, scipy, pandas, all libraries you'll want to be using. The Jupyter Notebook is nice for sharing quick and dirty calculations in a systematic format.
For serious debugging, I highly recommend PyCharm (which is free for students).
Thanks a lot for the advices!
What kind of academic research?
I may do poker research in the future, from judgement and decision making perspective, psych-oriented but may tough a bit on behavioral economics (not the hardcore maths stuffs tho). Maybe sth related to risk but I'm not sure yet.