Ebook Download Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, by Eric Siegel

Ebook Download Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, by Eric Siegel

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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, by Eric Siegel

Ebook Download Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, by Eric Siegel

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From the Back Cover

TRANSLATED INTO 12 LANGUAGES USED IN COURSES AT MORE THAN 30 UNIVERSITIES In this rich, fascinating—and surprisingly accessible—introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Trendsetters like Chase, Facebook, Google, HP, IBM, Match.com, Netflix, the NSA, Pfizer, Target, and Uber are seizing upon the power of big data to predict human behavior—including yours. Why? Predictive analytics reinvents industries and runs the world. Read on to discover how it combats risk, boosts sales, fortifies healthcare, optimizes social networks, toughens crime fighting, and wins elections. "What Nate Silver did for poker and politics, this does for everything else." —David Leinweber, author of Nerds on Wall Street "The Freakonomics of big data." —Stein Kretsinger, founding executive, Advertising.com "A deeply informative dive into a topic that is critical to virtually every sector of business today." —Geoffrey Moore, author of Crossing the Chasm "Moneyball for business, government, and healthcare." —Jim Sterne, founder, eMetrics Summit Learn more: www.ThePredictionBook.com

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About the Author

ERIC SIEGEL, PhD, founder of the Predictive Analytics World conference series and executive editor of The Predictive Analytics Times, makes the how and why of predictive analytics (aka machine learning) understandable and captivating. He is the author of the award-winning Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, a former Columbia University professor who used to sing to his students, and a renowned speaker, educator, and leader in the field. Eric has appeared on Al Jazeera America, Bloomberg TV and Radio, Business News Network (Canada), Fox News, Israel National Radio, National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet. He and his book have been featured in Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, The Financial Times, Forbes, Forrester, Fortune, Harvard Business Review, The Huffington Post, The New York Review of Books, Newsweek, Quartz, Salon, The San Francisco Chronicle, Scientific American, The Seattle Post-Intelligencer, The Wall Street Journal, The Washington Post, and WSJ MarketWatch. Follow him at @predictanalytic.

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Product details

Paperback: 368 pages

Publisher: Wiley; 2 edition (January 20, 2016)

Language: English

ISBN-10: 9781119145677

ISBN-13: 978-1119145677

ASIN: 1119145678

Product Dimensions:

5.9 x 1.1 x 8.8 inches

Shipping Weight: 1.1 pounds (View shipping rates and policies)

Average Customer Review:

4.0 out of 5 stars

319 customer reviews

Amazon Best Sellers Rank:

#32,006 in Books (See Top 100 in Books)

Eric Siegel is telling it all regarding predictive analytics. He suggests people are having their behavior examined and collected by corporations, political think tanks, etc. Great read!

I'm not really sure what the purpose of this book is. As far as I can tell it's just a loosely organized list of simplistic anecdotes related to predictive analytics. Did you know that Target can predict when you're pregnant!? Did you know that a guy put his life savings into a trading bot in the 90s!? That's about all I've learned from this book. The author goes too far in his quest for accessibility and removes any interesting details that would educate someone on how these models were conceived, built, or tested. I wasn't expecting a textbook, but this is book is so far in the other direction I just can't see how reading all 300+ pages is a productive use of anyone's time. Being interested in building predictive models myself, I've been sorely disappointed.Someone who is very non-technical may appreciate the author's style, but how many non-technical people are really going pick up a book titled "Predictive Analytics"?

As a professional with extensive operations & development background I wish I could have read this book when I began my journey into Data Science. I am someone who has used and built traditional business intelligence tools over the last fifteen years this book is fantastic at framing how Predictive Analytics is being used and for what specific business benefits.The book is intentionally not filled with math formulas (which may turn off some) but it focuses more on use cases of how the businesses around you are leveraging the data they already collect through daily operations. It's about how they are gaining a better insight into where their efforts are best spent to maximize their return on investment or capitalize on a previously masked rich subset of their existing customer base.If you're looking for a technical breakdown of how these algorithms work or are applied there are dozens of other books that Eric recommends as followup (referenced in probably the best notes section of any book I've ever seen).If you want a taste of the kind of information that you'll find in the book you should look on the Predictive Analytic World website for his keynote speech he did in Boston last year. It's a great book overview and convinced me to purchase the book.-Greg

I do (and teach) predictive analysis for a living and love this book, not because it has technical advice that I will use, but instead because it is a *beautifully* written introduction that I can give to people who have no technical background. It is a great book for a high school senior who is thinking about going into any applied mathematical area (like economics or biostatistics). If you have expertise in other areas and if you keep hearing about topics like machine learning or artificial intelligence, IBM Watson or the Netflix Prize and if you want to get a feel for the area this is the book for you.The quotes and examples frequently tie back into financial modeling but every domain is touched. Rather than being a book on math, this is equal parts history and social science. So, this book will be enjoyable for a wide audience.

A good book if you want to get a better understanding of artificial intelligence. It's is not an how-to-do-book but it will hopefully change how you think about data.

This book is a great introduction to how organizations use data about you, often provided by you, to determine your behavior. It talks about the many different areas that predictive analytics are used in from advertising to health care. If you don't understand why google or facebook are free you should read this book. If you have privacy concerns about those organizations you should read this book.If you already have a decent understanding of predictive analytics this book may not be what you are looking for but it does provide information on a number of sources do delve deeper into the subject. This book deepened my knowledge of predictive analytics and pointed me to a number of sources that I am checking out to learn even more.

Good read. This is a very good overview of where modern analytics is used and what it can do. I would have enjoyed more nuts and bolts of analytics though.

In respect of full disclosure I have known Eric for years in his capacity as founder of the Predictive Analytics World conference, and in my work in data mining and predictive analytics. That having been said, this is an excellent book for anyone who wants to learn what predictive analytics is, and how predictive analytics may be deployed across a wide range of disciplines. If you are looking for a hardcore set of algorithms or code examples this is not the book for you, and other reviewers have commented on that. I don't think that was the point of Eric's work. Eric's work does provide a review of what I think are the main pillars of predictive analytics; data, modeling, ensembles, uplift, unstructured data, deployment and ethics. If I had an issue with this book it would be in the ordering of the chapters, but, that is my personal view, and I can see why the book was structured the way that it was. The book will help you understand the major themes of predictive analytics, written in a way that let's the reader focus on the outcome, the advantages and the possibilities around predictive analytics. It is an 'easy' read yet still contains valuable insights. If you want to understand what people are talking about when they are talking about predictive analytics, read this book.

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