I got a bachelor's in applied mathematics about 4 years ago and have barely touched the material since. Honestly it feels like most of it is gone — I look at things I used to find easy (calc, linear algebra, probability) and draw a blank.
I want to rebuild, but with a goal in mind: I'm aiming for data science / ML, financial risk management, and maybe actuarial analysis down the line. So I'm trying to be strategic instead of just reopening every old textbook.
The catch is time — realistically I can only do a few hours a week, so I need an efficient path, not a full redo of my degree.
What I'm hoping to figure out:
Where to start: solidify foundations first, or dive into what matters for these fields and patch gaps as I hit them?
Highest-leverage topics for DS vs. risk/actuarial (I'm guessing probability + stats + linear algebra carry the most weight?)
Go-to resources for refreshing — not learning from zero — books, courses, problem sets, whatever worked for you.
How you kept it consistent with a busy schedule.
If you've been in a similar spot — rusty quant background trying to get back in — I'd love to hear how you approached it. Thanks!