I start from Principal Component Analysis. PCA is finding the axes of maximal eccentricity within a multidimensional space -- where the space is the variables describing some data set.

This is taking that _sort_ of approach and running it thoroughly out. What are topological and differential properties of a sample space, what can we do with the geometrical forms within that space, given what we know about (diffeomorphic) mappings, etc.