Linear Algebra
Vectors, matrices, and eigendecomposition power covariance analysis, PCA, and the multivariate models of later tracks.
Linear algebra is the grammar of multivariate finance. Vectors hold portfolios and time series of returns; matrices transform them — covariance matrices describe how assets move together, and their eigendecomposition powers PCA, which finds the dominant risk factors hidden in correlated markets. Solving linear systems underlies regression, Markowitz optimization, and risk decomposition. You need comfort with matrix multiplication, transpose and inverse, eigenvalues and eigenvectors, and positive definiteness. This node gives you the vocabulary that the probability and statistics nodes use next, and that the portfolio and risk tracks lean on throughout.