Seven sins of numerical linear algebra
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?"<p>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).<p>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.<p>(And don't get me started on all the weird linear algebra stuff they have going on in e.g. quantum physics.)<p>[1] <a href="https://missing.csail.mit.edu/about/" rel="nofollow">https://missing.csail.mit.edu/about/</a>