This textbook is a well-rounded rigorous and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth presenting proofs of major theorems and subsequent derivations as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey! -Nicholas Hoell University of Toronto This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear and the text logically builds up regularization classification and decision trees. Compared to its probable competitors it carves out a unique niche. Adam Loy Carleton College The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science. Key Features: Focuses on mathematical understanding. Presentation is self-contained accessible and comprehensive. Extensive list of exercises and worked-out examples. Many concrete algorithms with Python code. Full color throughout. Further Resources can be found on the authors website: https://github. com/DSML-book/Lectures | Data Science and Machine Learning Mathematical and Statistical Methods