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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. (Anglais) Relié – 8 mars 2013

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Praise for Predictive Analytics

"What Nate Silver did for poker and politics, this does for everything else. A broad, well–written book easily accessible to non–nerd readers."
DAVID LEINWEBER, author of Nerds on Wall Street: Math, Machines and Wired Markets

"This book is an operating manual for twenty–first–century life. Drawing predictions from big data is at the heart of nearly everything, whether it′s in science, business, finance, sports, or politics. And Eric Siegel is the ideal guide."
STEPHEN BAKER, author of The Numerati and Final Jeopardy: Man vs. Machine and the Quest to Know Everything

"Simultaneously entertaining, informative, and nuanced. Siegel goes behind the hype and makes the science exciting."
RAYID GHANI, Chief Data Scientist, Obama for America 2012 Campaign

"This is Moneyball for business, government, and healthcare."
JIM STERNE, founder, eMetrics Summit; chairman, Digital Analytics Association

"Predictive Analytics is not only a deeply informative dive into a topic that is critical to virtually every sector of business today, it is also a delight to read."
GEOFFREY MOORE, author of Crossing the Chasm

"The future is right now you′re living in it. Read this book to gain understanding of where we are and where we′re headed."
ROGER CRAIG, record–breaking analytical Jeopardy! champion; CEO, Cotinga

Présentation de l'éditeur

Mesmerizing & fascinating. . .
The Seattle Post–Intelligencer

The Freakonomics of big data.

Stein Kretsinger, founding executive of Advertising.com


∗∗∗Winner of the Nonfiction Book and Small Business Book Awards∗∗∗

∗∗Used in courses at more than 30 universities∗∗

∗Translated into 9 languages∗


An introduction for everyone

In this rich, fascinating surprisingly accessible introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Rather than a how to for hands–on techies, the book serves lay readers and experts alike by covering new case studies and the latest state–of–the–art techniques.

Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you′re going to click, buy, lie, or die.

Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections.

How? Prediction is powered by the world′s most potent, flourishing unnatural resource: data. Accumulated in large part as the by–product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.

Predictive analytics
 unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.

In this lucid, captivating introduction now in its Revised and Updated edition former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:

  • What type of mortgage risk Chase Bank predicted before the recession
  • Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves
  • Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights
  • Five reasons why organizations predict death including one health insurance company
  • How U.S. Bank and Obama for America calculated and Hillary for America 2016 plans to calculate the way to most strongly persuade each individual
  • Why the NSA wants all your data: machine learning supercomputers to fight terrorism
  • How IBM′s Watson computer used predictive modeling to answer questions and beat the human champs on TV′s Jeopardy!
  • How companies ascertain untold, private truths how Target figures out you′re pregnant and Hewlett–Packard deduces you′re about to quit your job
  • How judges and parole boards rely on crime–predicting computers to decide how long convicts remain in prison
  • 183 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more

How does predictive analytics work? This jam–packed book satisfies by demystifying the intriguing science under the hood. For future hands–on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.

A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it or consumed by it get a handle on the power of Predictive Analytics.

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3.3 étoiles sur 5
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Format: Relié
Ce n'est pas un ouvrage technique sur le "machine learning" mais un panorama du potentiel de l'analyse prédictive. L'ouvrage est organisé en 7 chapitres qui détaillent 7 cas d'entreprises. En partie centrale sont présentés 147 exemples d'analyse prédictives dans 9 domaines d'applications. De quoi donner des idées.

La webographie en annexe est à elle seule une somme sur le sujet de l'analyse prédictive.

Seul regret : des exemples anglo-saxons mais qui parleront à tous en les transposant dans des contextes européens.

Un ouvrage plus abordable et facile à lire que le livre (néanmoins très intéressant notamment sur les biais et erreurs en prédiction) de Nate Silver "The Signal and the Noise: The Art and Science of Prediction" http://www.amazon.fr/The-Signal-Noise-Science-Prediction/dp/0141975652/ref=sr_1_1?ie=UTF8&qid=1410273781&sr=8-1&keywords=nate+silver
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I'm a student writing a thesis on Predictive Analytics and this is an amazing book for students and experts.
Well written and easy to understand
Must-read !
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L'abonnement premium est sensé livrer sous 1 ou 2 jours. Cela fait plus d'1 mois que j'attends. Vous êtes de voleurs. Désolé, cela ne concerne pas directement la qualité de l'ouvrage (que je ne peux pas jugé étant donné qu'il n'est jamais arrivé) mais bien AMAZON.
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Commentaires client les plus utiles sur Amazon.com (beta)

Amazon.com: 4.1 étoiles sur 5 288 commentaires
2 internautes sur 2 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 An engaging and occasionally wacky deep dive into the world of predictive analytics 6 mai 2016
Par Spine - Publié sur Amazon.com
Format: Broché Achat vérifié
Predictive analytics, risk modeling, and other burgeoning fields based on Big Data are making a lot of people nervous these days--for good reason. But the beneficial applications of these methods can't be denied, and Eric Siegel makes a very persuasive case for those benefits in this surprisingly approachable and funny book, which packs a lot of interesting anecdotes and case studies. It's definitely not a textbook or technical manual. The book will appeal most to people who already have an interest in this area or who find any subject inherently fascinating if it's presented well.
1 internautes sur 1 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Five Stars 25 août 2015
Par Anita Raquel - Publié sur Amazon.com
Format: Relié Achat vérifié
As time has gone by, I've found myself going back again and again to refer to specific points discussed in this book. It was a bit heavy at first, thick with facts that I found irritating and contradictory to certain favorite and closely held biases of mine, but over time, I could see his points better and better, in spite of myself.
Life isn't fair, and people certainly aren't. The ways that they react to things reflects this to a degree that would surprise even the coldest eyed cynic, and there it is- the thing that bothered me so much....but it's best if you face it. There are some pleasant discoveries in here too, but I think the most important aspect is illusion busting. Those sweet daydreams about how things should be, might be exactly what is holding you back.
Forewarned is forearmed, and the information in here is of a hefty caliber. Use it well.
Yes, I did actually buy this book, and it was worth every penny.
13 internautes sur 14 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 An Excellent Book by a Knowledgeable Author 29 mai 2013
Par Timothy W. Daciuk - Publié sur Amazon.com
Format: Relié Achat vérifié
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.
1 internautes sur 1 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Well worth reading and no math needed 11 février 2014
Par fitzalling - Publié sur Amazon.com
Format: Relié Achat vérifié
Dr. Siegel seems to have written this book for those with limited math skills, but with a desire to better understand the techniques for extracting meaning from big data. Since this describes me, I found the book quite valuable and gave it my highest rating. If you already have a strong grasp of the tools for organizing and interpreting big data, the book will probably not meet your needs.

While the author writes well, the Introduction and first chapter skipped around on topics and anecdotes which caused me some initial concern. However, keep going because once past this early stage, the book gained traction quickly. In chapter 2, the author considers ethical concerns arising from predictive analysis. Target's analysis of a woman's buying patterns for pregnancy and Hewlett-Packard's analysis of its own employees for those that may quit both raise thought-provoking issues of whether such analyses are, to use the author's phrase - insight or intrusion. Using predictive analytics to prevent online fraud probably isn't as controversial.

The author describes the tools to undertake predictive analysis. Decision trees, ensembles, and ensembles of ensembles may all be used to draw meaning from data. He describes the IBM team's development of Watson for the famous contest on the game show "Jeopardy" when Watson beat two humans who had performed at championship levels on this show. The author details the challenges of natural language processing to enable a machine to derive meaning from spoken English. He goes through examples that illustrate the high-level challenge. The IBM team used the tools of ensembles of ensembles (read the book to understand this) coupled with statistical interpretation to determine the most likely correct answer to any question and to do so faster than the human contestants. This was machine learning at the currently highest level. One of the fascinating points is that art drives machine learning.

Can predictive analytics be employed to forecast an individual's actions? The thought seems troubling to me, but the possibility that prediction could be so used must be recognized. Which persons are most likely to favorably respond to a cell phone renewal offer as opposed to interpreting the offer as an opportunity to seek another carrier could have meaningful financial implications to a telecommunications carrier. He describes a predictive modeling undertaken by Oregon to predict which potential parolee is more likely to commit another crime if released from prison. This, too, has real world implications for the potential parolee and society. Once again, predictive analytics challenged me on many levels.

Dr. Siegel identifies five effects of prediction. These are: (1) the prediction effect; (2) the data effect; (3) the induction effect; (4) the ensemble effect; and (5) the persuasion effect. I encourage you to read the book to learn about these effects and consider their potential cumulative "effect" on society, for good and ill.
1 internautes sur 1 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Predictive Analytics 2 mars 2014
Par Stewart Paulson - Publié sur Amazon.com
Format: Format Kindle Achat vérifié
Eric Siegel describes the historical use and proven value of predictive analytics and describes the how, why and when predictive analytics, has been used as an effective tool for improving decision making in social institutions as well as retail, and commercial/industrial business. One of the great strengths of this book is that Eric Siegel provides a wealth of links to web sites where one can find additional information on predictive analytics, written by the leaders/experts in this field . An objective, well researched and well written book . Predictive analytics is described as a tool that provides management with more information for intelligent and accountable decision making. Predictive analytics has been proven in sports and elections as well as in insurance, finance, marketing and medicine as having huge potential for assisting management in making better decisions, particularly with respect to strategy and resource allocation. Siegel describes how social networking is being used to enhance decision making in marketing and providing management with a better understanding of the needs and preferences of their customers. He also discusses the difficulties that have been faced by the proponents of predictive analytics and how he and other proponents have dealt with critics, such as media that have express concern over privacy issues. Predictive analytics, like many advancements have faced regulatory changes and call for disciplined moral responsibility in their application, particularly where social communication is being integrated with other data bases for decision making by corporate and social/political leaders. With this sidebar Siegel goes on to focus on the tremendous benefits of predictive analytics and the numerous areas it is being used in with great success. This book is a six star book in a 5 star world.
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