Steven S. Skiena is Distinguished Teaching Professor of Computer Science at Stony Brook University. He is the author of four well-regarded books: The Algorithm Design Manual (2008), Calculated Bets: Computers, Gambling, and Mathematical Modeling to Win (2001), Programming Challenges (with Miguel Revilla, 2003) and Computational Discrete Mathematics (with Sriram Pemmaraju, 2003). Skiena heads the Lydia news/blog analysis project at Stony Brook, using large-scale text analysis to chart the frequency, sentiment and relationships among millions of people, places, and things. This technology forms the foundation of General Sentiment (http: //www.generalsentiment.com), where he serves as co-founder and Chief Scientist. Lydia news analysis has been applied to several social science research projects, including financial forecasting and presidential election analysis. The rankings underlying Who's Bigger? derive from this analysis.

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.

The Data Science Design Manualis a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles.

This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.

Additional learning tools:

Contains “War Stories,” offering perspectives on how data science applies in the real worldIncludes “Homework Problems,” providing a wide range of exercises and projects for self-studyProvides a complete set of lecture slides and online video lectures at www.data-manual.comProvides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapterRecommends exciting “Kaggle Challenges” from the online platform KaggleHighlights “False Starts,” revealing the subtle reasons why certain approaches failOffers examples taken from the data science television show “The Quant Shop”(www.quant-shop.com)