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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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
Well written and easy to understand
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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.
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.