Just for the record, from the first page:
"Background material needed for an undergraduate course has been put in the appendix.
For this reason, the appendix has homework problems."
The appendix covers Probability and Linear Algebra.
The appendix covers Probability and Linear Algebra.
Statistics is a powerful lens through which to view all data science. E.g. supervised learning is building a model of the conditional probability P(y|x). Again, I am biased, but I think that methods that do not have some statistical interpretation are unlikely to be useful. E.g. if we take the graph of Facebook users and apply some matrix decomposition algorithm, who cares? What can we do with this decomposition? What does it predict?
E.g. people used to say "neural networks are a simple, flexible functional form for y = f(X,theta)". This turned out to be wrong: SGD training of neural networks has more advantages than the flexibility of the functional form. But it was a good hypothesis and starting point.
SVMs and decision trees have no statistical justification I know of. Low rank matrix approximation and k-means are justified by latent variables and non-parametric kernel methods respectively. I agree these justifications came after the fact, but they do give a way to understand how these models work.
Most importantly, all of the small tasks surrounding training a model are purely statistical, e.g. cross validation, different measures of accuracy, handling endogenous variables, etc.
Are you trying to argue that statistics is its own field and not built upon mathematics?
If I have not studied statistics I will not have a solid foundation in statistics, even if I have studied probability and linear algebra.