Have you checked out the detailed table of contents for my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die?


Predictive Analytics
Table of Contents

Foreword       Thomas H. Davenport xiii

What is the occupational hazard of predictive analytics?

The Prediction Effect

How does predicting human behavior combat risk, fortify healthcare, toughen crime fighting, and boost sales? Why must a computer learn in order to predict? How can lousy predictions be extremely valuable? What makes data exceptionally exciting? How is data science like porn? Why shouldn't computers be called computers? Why do organizations predict when you will die?

Chapter 1
Liftoff! Prediction Takes Action (deployment)

How much guts does it take to deploy a predictive model into field operation, and what do you stand to gain? What happens when a man invests his entire life savings into his own predictive stock market trading system?

Chapter 2
With Power Comes Responsibility: Hewlett-Packard, Target, and the Police Deduce Your Secrets (ethics)

How do we safely harness a predictive machine that can foresee job resignation, pregnancy, and crime? Are civil liberties at risk? Why does one leading health insurance company predict policy holder death? An extended sidebar on fraud detection addresses the question: how does machine intelligence flip the meaning of fraud on its head?

Chapter 3
The Data Effect: A Glut at the End of the Rainbow (data)

We are up to our ears in data, but how much can this raw material really tell us? What actually makes it predictive? Does existing data go so far as to reveal the collective mood of the human populace? If yes, how does our emotional online chatter relate to the economy's ups and downs?

Color Book Insert
147 Examples of Predictive Analytics

A cross-industry compendium of 147 mini-case studies in predictive analytics, divided by vertical:

  • Personal Life
  • Marketing
  • Finance
  • Healthcare
  • Crime Fighting and Fraud Detection
  • Reliability Modeling
  • Government and Nonprofit
  • Human Language and Thought
  • Human Resources
Chapter 4
The Machine That Learns: A Look Inside Chase's Prediction of Mortgage Risk (modeling)

What form of risk has the perfect disguise? How does prediction transform risk to opportunity? What should all businesses learn from insurance companies? Why does machine learning require art in addition to science? What kind of predictive model can be understood by everyone? How can we confidently trust a machine's predictions? Why couldn't prediction prevent the global financial crisis?

Chapter 5
The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles)

To crowdsource predictive analytics—outsource it to the public at large—a company launches its strategy, data, and research discoveries into the public spotlight. How can this possibly help the company compete? What key innovation in predictive analytics has crowdsourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds?

Chapter 6
Watson and the Jeopardy! Challenge (question answering)

How does Watson—IBM's Jeopardy!-playing computer—work? Why does it need predictive modeling in order to answer questions, and what secret sauce empowers its high performance? How does the iPhone's Siri compare? Why is human language such a challenge for computers? Is artificial intelligence possible?

Chapter 7
Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift)

What is the scientific key to persuasion? Why does some marketing fiercely backfire? Why is human behavior the wrong thing to predict? What should all businesses learn about persuasion from presidential campaigns? What voter predictions helped Obama win in 2012 more than the detection of swing voters? How could doctors kill fewer patients inadvertently? How is a person like a quantum particle? Riddle: What often happens to you that cannot be perceived, and that you can't even be sure has happened afterward—but that can be predicted in advance?


Ten Predictions for the First Hour of 2020

A. Five Effects of Prediction 221
B. Twenty-One Applications of Predictive Analytics 222
C. Prediction People—Cast of "Characters" 225
Notes 228
Acknowledgments 290
About the Author 292
Index 293


FAQ: Is this book for practitioners and experts?

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