We thank the many people who assisted us in improving the book from its inception as Data Mining for Business Intelligence in 2006 (using XLMiner, now Analytic Solver), through the recent editions now called Data Mining for Business Analytics, including two later XLMiner editions, a JMP edition, an R edition, and now for the first time, a Python edition.

Anthony Babinec, who has been using earlier editions of this book for years in his data mining courses at Statistics.com, provided us with detailed and expert corrections. Dan Toy and John Elder IV greeted our project with early enthusiasm and provided detailed and useful comments on initial drafts. Ravi Bapna, who used an early draft in a data mining course at the Indian School of Business and later at University of Minnesota, has provided invaluable comments and helpful suggestions since the book’s start.

Many of the instructors, teaching assistants, and students using earlier editions of the book have contributed invaluable feedback both directly and indirectly, through fruitful discussions, learning journeys, and interesting data mining projects that have helped shape and improve the book. These include MBA students from the University of Maryland, MIT, the Indian School of Business, National Tsing Hua University, and Statistics.com. Instructors from many universities and teaching programs, too numerous to list, have supported and helped improve the book since its inception. Scott Nestler has been a helpful friend of this book project from the beginning.

Kuber Deokar, instructional operations supervisor at Statistics.com, has been unstinting in his assistance, support, and detailed attention. We also thank Anuja Kulkarni, assistant teacher at Statistics.com. Valerie Troiano has shepherded many instructors and students through the Statistics.com courses that have helped nurture the development of these books.

Colleagues and family members have been providing ongoing feedback and assistance with this book project. Boaz Shmueli and Raquelle Azran gave detailed editorial comments and suggestions on the first two editions; Bruce McCullough and Adam Hughes did the same for the first edition. Noa Shmueli provided careful proofs of the third edition. Ran Shenberger offered design tips. Che Lin and Boaz Shmueli provided feedback on Deep Learning. Ken Strasma, founder of the microtargeting firm HaystaqDNA and director of targeting for the 2004 Kerry campaign and the 2008 Obama campaign, provided the scenario and data for the section on uplift modeling. We also thank Jen Golbeck, director of the Social Intelligence Lab at the University of Maryland and author of Analyzing the Social Web, whose book inspired our presentation in the chapter on social network analytics. Randall Pruim contributed extensively to the chapter on visualization. Inbal Yahav, co-author of the R edition, helped improve the social network analytics and text mining chapters.

Marietta Tretter at Texas A&M shared comments and thoughts on the time series chapters, and Stephen Few and Ben Shneiderman provided feedback and suggestions on the data visualization chapter and overall design tips.

Susan Palocsay and Mia Stephens have provided suggestions and feedback on numerous occasions, as have Margret Bjarnadottir, and, specifically for this Python edition, Mohammad Salehan. We also thank Catherine Plaisant at the University of Maryland’s Human–Computer Interaction Lab, who helped out in a major way by contributing exercises and illustrations to the data visualization chapter. Gregory Piatetsky-Shapiro, founder of KDNuggets.com, has been generous with his time and counsel in the early years of this project.

We thank colleagues at the MIT Sloan School of Management for their support during the formative stage of this book—Dimitris Bertsimas, James Orlin, Robert Freund, Roy Welsch, Gordon Kaufmann, and Gabriel Bitran. As teaching assistants for the data mining course at Sloan, Adam Mersereau gave detailed comments on the notes and cases that were the genesis of this book, Romy Shioda helped with the preparation of several cases and exercises used here, and Mahesh Kumar helped with the material on clustering.

Colleagues at the University of Maryland’s Smith School of Business: Shrivardhan Lele, Wolfgang Jank, and Paul Zantek provided practical advice and comments. We thank Robert Windle, and University of Maryland MBA students Timothy Roach, Pablo Macouzet, and Nathan Birckhead for invaluable datasets. We also thank MBA students Rob Whitener and Daniel Curtis for the heatmap and map charts.

Anand Bodapati provided both data and advice. Jake Hofman from Microsoft Research and Sharad Borle assisted with data access. Suresh Ankolekar and Mayank Shah helped develop several cases and provided valuable pedagogical comments. Vinni Bhandari helped write the Charles Book Club case.

We would like to thank Marvin Zelen, L. J. Wei, and Cyrus Mehta at Harvard, as well as Anil Gore at Pune University, for thought-provoking discussions on the relationship between statistics and data mining. Our thanks to Richard Larson of the Engineering Systems Division, MIT, for sparking many stimulating ideas on the role of data mining in modeling complex systems. Over two decades ago, they helped us develop a balanced philosophical perspective on the emerging field of data mining.

Lastly, we thank the folks at Wiley for the decade-long successful journey of this book. Steve Quigley at Wiley showed confidence in this book from the beginning and helped us navigate through the publishing process with great speed. Curt Hinrichs’ vision, tips, and encouragement helped bring this book to the starting gate. Sarah Keegan, Mindy Okura-Marszycki, Jon Gurstelle, Kathleen Santoloci, and Katrina Maceda greatly assisted us in pushing ahead and finalizing this Python edition. We are also especially grateful to Amy Hendrickson, who assisted with typesetting and making this book beautiful.