Almost every quant model is a matrix problem in disguise: a portfolio is a vector, a covariance is a matrix, a regression is a projection. This skill builds the ideas with short checks (matrices you fill in, numbers you compute, expressions you type) and finance examples, then turns them into algorithms: LU, QR and Cholesky. It ends with a code lab that fits a regression on real stock returns using only your own solver and checks it against NumPy.
