I wish there was a Missing Semester of Linear Algebra course to help people go from "Okay I have a course or two in linear algebra, I know what span, vectors, basis and dimension mean, the formal definition of an inner product space, and I can do Gauss-Jordan elimination, determinants and eigenvalues for small matrices with paper and pencil" to "I have a 100x100 matrix of noisy data from sensors and this research paper I found tells me I can do some fantastic stuff if I compute such-and-such involving eigenvalues or inverses or whatnot. Or maybe I have a process with 1000 states where I know the probability objects move from state i to state j for each pair (i, j) and I want to find the steady state. How do I wrangle numpy into doing what I need?"

MIT has a course called The Missing Semester of Your CS Education [1]. It tells you about practical stuff that you need to know but isn't really taught in classes (shells, version control, build systems, package managers, VM's).

There needs to be something similar for linear algebra, it seems like there's a lot of folk knowledge and a big gap between what typical undergrad courses train you to do and what you encounter in actual practical problems.

(And don't get me started on all the weird linear algebra stuff they have going on in e.g. quantum physics.)

[1] https://missing.csail.mit.edu/about/