They wrote the books Elements of Statistical Learning and Introduction to Statistical Learning in R. Those books are about least squares regression, clustering, decision trees, random forests, boosting, additive models, support vector machines, etc.

All these are common statistical learning methods used in Data Science.

Also, if you read the fantastic computer age statistical inference from Efron & Hastie (it's available online!), you notice they are both fans of data-science! The whole book reads like a big argument why we need data-science and why traditional statistics is not always the answer.

This comes especially obvious in the epilogue, where they try to give a quick oververview how the concept of a "data-science" formed and how statistics diverged into data-science + ML and the traditional statistics-community.

They end the book by arguing that both communities should find to each other, because fundamentally they try to do similiar things. I also think is badly needed. Unfortuntaly I experience some of arrogance on both sides, which makes it harder! (DS/ML-people have no idea what they are doing and only throw their algorithms on problems & benchmark them! Statisticians are obsolete & i can just automate them with NNs!)