• SECOND BOOK: The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. The success rate of new machine learning projects is dismal. The AI Playbook offers a solution. It presents a six-step practice fit for wide adoption that ushers machine learning projects from conception to deployment. Author Eric Siegel calls this practice bizML. Click here to request review copies from MIT Press / Penguin Random House (you'll have to enter the book's ISBN: 978-0262048903).
• NEW CURRICULUM SUPPLEMENT RELATED TO SECOND BOOK: A University Curriculum Supplement to Teach a Business Framework for ML Deployment. This article presents a "field tested" curriculum supplement designed to augment introductory data science courses so that they cover the business-side execution of machine learning projects -- the know-how needed to ensure successful deployment.
• COURSE: Machine Learning Leadership and Practice — End-to-End Mastery. This end-to-end course from author Eric Siegel will empower you to launch machine learning. Accessible to business-level learners and yet vital to techies as well, it covers both the state-of-the-art techniques and the business-side best practices. Inquire about integrating this curriculum into yours.
• "Revised and Updated" edition FAQ
  • What's new?
  • Who's this book for?
  • Is this book a how-to?
• The book's figures: View or download in high res
• Machine learning glossary — this comprehensive glossary by author Eric Siegel covers the concepts of, and beyond the scope of, Predictive Analytics.
• The book's Notes: 120 pages of citations and comments
• The book's honors and awards
• Request an evaluation copy from the publisher
• For other questions — or if the publisher is slow delivering your evaluation copy — contact the author, Eric Siegel.

BONUS: Watch the author's popular geek rap music video for a substantively educational song about predictive analytics that engages students

What professors should know about this book:

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die has served as a textbook at more than 30 colleges and universities (18+ as required reading). A former computer science professor at Columbia University, author Eric Siegel wrote this introduction to be conceptually complete. In the table of contents, the words in parentheses aside each chapter's "catchy" title reveal an outline that covers the fundamentals: 1) model deployment, 2) ethics, 3) data, 4) predictive modeling, 5) ensemble models, 6) question answering, and 7) uplift modeling.

However, this book is not in the formal style of a textbook. Much to the contrary, the author sought to deliver an entertaining, engaging, relevant work that illustrates the concepts largely via anecdotes.

To guide reading assignments, here are the chapter dependencies:

Predictive Analytics delivers unique value as what may be the only book that provides all three of the following:

1. Fully accessible and engaging to non-technical readers, new-comers of any background
2. Conceptually complete coverage of the core technical concepts
3. Full industry overview of predictive analytics' value across all sectors; focuses on real-world usage, illustrated via hundreds of brand-name examples

New content: A much-needed warning regarding bad science. Among other additions, the Revised and Updated edition's Chapter 3, "The Data Effect," now includes an in-depth section about an all-too-common pitfall and how we avoid it: How to successfully tap data's potential without being fooled by random noise, ensuring sound discoveries are made. This issue could not be more important for our next emerging generation of practitioners.

After reviewing a near-final draft of this new book section, Dr. A J Nigl of the University of California, Irvine's Business School Extension Program said, "An excellent job of handling such a potentially complicated topic in a non-technical manner sprinkled with Siegel's usual healthy dose of humor... and will cause readers to think about potential pitfalls in the interpretation of predictive analytics and modeling outcomes. I respect the author's desire to make sure new entrants to the field of predictive analytics understand the need to address issues like spurious outcomes and illusory predictive relationships. It is a very important topic and he did a great job explaining it without being overly technical or pedantic. The true assessment of a book chapter's worth is if it makes people think and this chapter revision certainly made me think."

BONUS: If you adopt Predictive Analytics as a text for your course, you can arrange for free access to the audiobook version for your students by contacting the author, Eric Siegel.