I'm from an engineering background, going in the direction of data scientist. Sometimes I find that my math skills could be stronger, and I try to read up on things when I encounter them, but still it sometimes feels like there is an infinite amount to learn. Maybe I could use some more systematic approach to it. Anyone else who has walked this path, and could come with some useful advice/resources?
On the linear algebra front, you get some understanding from your first course, but the more you internalize it by meeting the ideas in different contexts, the more useful it will be. A decent amount of higher math is turning things into almost-linear problems, and then trying to sort out the parts which don't quite fit...
Just take statistic, especially Multivariate for big data (big data for statisticians is huge predictors not petabyte of observations).
Math people can do so much in data science. I think statistic is better for a non math person and it's much better suited for data. Since statistic is all about data.
I can give recommendations on books for the former but not the latter.
Check out the syllabuses and self-study at your own pace