Strange question, but I've found myself with quite a bit of spare time recently and want some input on how to best spend it. Also, some may say just follow the markets/prep for buyside recruiting, and these options are for things to do in addition to that, rather than in spite of that. My options, and I am open to alternatives, are as follows:
Learn a language. Always wanted to learn a language. Would first want to learn Spanish/French, and perhaps more down the line. The most "fun" of the choices in terms of using it when traveling/to pick up girls.
Learn to code. Would arguably be the best long-term when looking at my finance career, and would pad my resume for buyside recruiting. No background related to computer science or anything of the sort.
Read. Big fan of reading and have a long list of books I want to read (finance ones include The Man Who Knew: The Life and Times of Alan Greenspan, More Money Than God, Howard Marks' new book). Could get through a book or 2 a week if I spent my spare time solely on reading.
Of course, these aren't necessarily mutually exclusive - I could spend 1 hour a day learning a language and 1 hour a day learning to code. Or 1 hour a day reading and 1 hour a day learning to code. I go to the gym 5/6 days a week so I don't think I have enough spare time for an hour of all 3 + the gym in a day. Any thoughts are appreciated!
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Learn to code 1st. You can learn the basics of Python from this freecodecamp youtube video (4-5 hours) plus another 12 hours for practice. You can do this in a weekend if you take adderall and have nothing else to do.
That will get you to the point where you can call yourself "proficient enough" in Python. Then take a basic data science course (free to audit on Coursera, edX, and a couple other MOOC platforms...each about 20 hours of video classes) and learn how to build a basic neural network using tensor flow with Keras backend architecture (keras wraps tensorflow for simplicity).
This 3Blue1Brown youtub channel has a great 4 video playlist that makes neural nets understandable to people that don't know any of the math (you don't need to..that's what the neural network is for) or any of the programming.
https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_6700…
And This guy does a similar thing, but does the code right in front of you as he lectures (so better to watch after you learn Python from the 1st course linked up above). This last video series is best to watch AFTER you've taken the theoretical intro course on data science (because he jumps into code and goes pretty fast).
very detailed response. If OP doesn't utilize it I probably will. +1
Thanks for the response, this is really helpful
Could you propose a similar "path" for R for data analysis?
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