I read this the first time posted (didn't get any comments), very poignant, especially the part about relearning trig/geometry, precalc. There's boundless resources for learning now but you don't get little endorphin/epinephrine releases like you do when your gcc/clang/VS compile succeeds, it's still mostly notebooks, whiteboards, pencils and 4-color pens (tho i've seen lots of cool JS animations, ipython notebooks, and libs in R, matlab/octave and now julia)

- a few universities have put (many/most) of lecture notes and student notes up: http://www.maths.cam.ac.uk/studentreps/res/notes.html as well as study guides: http://www.maths.cam.ac.uk/undergrad/studyskills/text.pdf

- books about how to think like a mathematician: Keith Devlin, Kolmogorov/Alexandrov et al did 2 Dover books, and Houston: http://www.amazon.com/How-Think-Like-Mathematician-Undergrad... and http://www.amazon.com/How-Study-as-Mathematics-Major/dp/0199... and Ellenberg: http://www.amazon.com/How-Not-Be-Wrong-Mathematical/dp/15942...

- Concrete Math by Patashkin, Knuth et al; Streetfighting Math by Mahajan and his newer, freely available: http://mitpress.mit.edu/books/art-insight-science-and-engine...

- this machine learning/data science list: http://www.reddit.com/r/MachineLearning/comments/1jeawf/mach...

- Cal newport blog: http://calnewport.com/blog/2012/10/26/mastering-linear-algeb...

- besides Dover, Schaum Outlines are a good cheap resource abundantly available in used bookstores(tho there are in fact some type-ridden ones also)

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the best advice general advice i've seen is the same as what they tell you in college: form study groups and make commitments to regular discussion. Stronger students strengthen their understanding by tutoring others at the whiteboard. There's lots of machine learning and data sciencey meetups and informal groups springing up e.g.http://machine-learning.meetup.com/