Books in the Chapman & Hall/CRC Data Science Series series

Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable reference for researchers involved with model selection, variable selection, machine learning, and risk management.

The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book.

"A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source"--

Highlights a combination of two aspects: technical concreteness and holistic thinking. Authors discuss what principles are used to invent these techniques, what assumptions are made, how mathematics is used to articulate these assumptions, and how these formulations generalize a range of real-world applications into generic and abstract forms.