High-school dropout with a phd in a social science here. My grades weren’t good enough to study math during undergrad and I was dealing with a chronic illness that meant I didn’t have my shit together enough to transfer into a useful degree either. I’m a machine learning scientist at a company you’ve heard of and probably spent money on (not FAANG). Math is a big part of my day job.
Here’s what I did to learn math. I wouldn’t recommend this path. You will have a much easier time getting a job with a quantitative degree. You’re only 26, so you can go back to school without really losing much time. If you must do it this way, do the exercises, do the exercises, do the exercises, build a portfolio, and do the exercises.
Calculus/Analysis
Stewart single var calculus, then the multivar book. These are easy starters
Real analysis: Series, Functions of Several Variables, and Applications. Miklós Laczkovich, Vera T. Sós
Spivak Calc and Calc on Manifolds books
Bonus: Advanced Calculus A Geometric View, Callahan. This is what I turn to when I want to punish myself or remind myself how certain analysis proofs go.
Linear Algebra
Strang, Intro to LA is great. Start with this one
Strang, LA and learning from data. Will be tough without the first book
Stats
Hogg, Introduction to Mathematical Statistics
Gelman et al, BDA3 for Bayes
ML
Bishop PRML and Elements of statistical learning. Do the exercises. Build the algos in Python.
Here’s what I did to learn math. I wouldn’t recommend this path. You will have a much easier time getting a job with a quantitative degree. You’re only 26, so you can go back to school without really losing much time. If you must do it this way, do the exercises, do the exercises, do the exercises, build a portfolio, and do the exercises.
Calculus/Analysis
Stewart single var calculus, then the multivar book. These are easy starters
Real analysis: Series, Functions of Several Variables, and Applications. Miklós Laczkovich, Vera T. Sós
Spivak Calc and Calc on Manifolds books
Bonus: Advanced Calculus A Geometric View, Callahan. This is what I turn to when I want to punish myself or remind myself how certain analysis proofs go.
Linear Algebra
Strang, Intro to LA is great. Start with this one
Strang, LA and learning from data. Will be tough without the first book
Stats
Hogg, Introduction to Mathematical Statistics
Gelman et al, BDA3 for Bayes
ML
Bishop PRML and Elements of statistical learning. Do the exercises. Build the algos in Python.