  These notes are an overview of some classical linear methods in Multivariate
Data Analysis. This is a good old domain, well established since the 60's, and
refreshed timely as a key step in statistical learning. It can be presented as
part of statistical learning, or as dimensionality reduction with a geometric
flavor. Both approaches are tightly linked: it is easier to learn patterns from
data in low dimensional spaces than in high-dimensional spaces. It is shown how
a diversity of methods and tools boil down to a single core methods, PCA with
SVD, such that the efforts to optimize codes for analyzing massive data sets
like distributed memory and task-based programming or to improve the efficiency
of the algorithms like Randomised SVD can focus on this shared core method, and
benefit to all methods.
