Unites theory, algorithm design, and practical data analysis for simplicity and clarity of content
Contains more than twenty detailed and carefully crafted Python tutorials
Each chapter includes exercises of varying levels of difficulty
Uses publicly available data sets throughout the book

"This 430-page book contains an excellent collection of information on the subject of practical algorithms used in data science. The discussion of each algorithm starts with some basic concepts, followed by a tutorial with real datasets and detailed code examples in Python or R. Each chapter has a set of exercise problems so readers can practice the concepts learned in the chapter. ? a good reference for practitioners, or a good textbook for graduate or upper-class undergraduate students." (Xiannong Meng, Computing Reviews, September, 2017)

"This textbook on practical data analytics unites fundamental principles, algorithms, and data. ? this book is devoted to upper-division undergraduate and graduate students in mathematics, statistics, and computer science. It is intended for a one- or two-semester course in data analytics and reflects the authors' research experience in data science concepts and the teaching skills in various areas. ? The text is eminently suitable for self-study and an exceptional resource for practitioners." (Krzysztof J. Szajowski, zbMATH 1367.62005, 2017)