Linear algebra is one of the foundations of statistics and model building. Here's a thread on some fantastic resources (mostly books) to learn it (1/n)
I think Kuldeep Singh's Linear Algebra Step by Step should be the first step in learning LA; it's a wonderful and very gentle introduction.
After you've read Singh's book, you probably won't understand much about LA, but at least you'll be familiarized with all the important topics tinyurl.com/vev4w7n
While Linear Algebra Step by Step is very applied and practical, I think it's important to get a conceptual grasp of the field as a second step. Two resources should help with this: @3blue1brown 's beautiful essence of linear algebra, and Sheldon Axler's Linear Algebra done right
@3blue1brown As a third step, I would recommend a book that's slightly more advanced, but that is truly wonderful: Gockenbach's Finite-Dimensional Linear Algebra. It builds on the same foundation as Axler's, but with a slightly more applied outlook (a bit more numerical analysis for instance)
@3blue1brown There are other books I love (Lax's Linear algebra and its applications, Loehr's Advanced linear algebra), but the three books mentioned above give a strong footing in the topic.
@3blue1brown After that, to bridge the gap between linear algebra and statistics, I would recommend two fantastic books: Adachi's very easy Matrix-Based Introduction to Multivariate Data Analysis, and...
@3blue1brown ... Magnus and Neudecker's Matrix Differential Calculus with Applications in Statistics and Econometrics.
@3blue1brown That was all. I spent a long time over the last three years trying to find great references on Linear algebra, so I hope this can be useful to other people! Linear algebra is amazingly powerful, and I think the resources cited help to appreciate its beauty! (n/n)
