Understanding the predictive analytics life cycle
著者
書誌事項
Understanding the predictive analytics life cycle
（Wiley and SAS business series）
John Wiley & Sons, c2014
- : hardcover
- タイトル別名
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Understanding the predictive analytics lifecycle
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
A high-level, informal look at the different stages of the predictive analytics cycle Understanding the Predictive Analytics Lifecycle covers each phase of the development of a predictive analytics initiative. Through the use of illuminating case studies across a range of industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail, the book successfully illustrates each phase of the predictive analytics cycle to create a playbook for future projects.
Predictive business analytics involves a wide variety of inputs that include individuals' skills, technologies, tools, and processes. To create a successful analytics program or project to gain forward-looking insight into making business decisions and actions, all of these factors must properly align. The book focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as input for human decisions. The book includes:
An overview of all relevant phases: design, prepare, explore, model, communicate, and measure
Coverage of the stages of the predictive analytics cycle across different industries and countries
A chapter dedicated to each of the phases of the development of a predictive initiative
A comprehensive overview of the entire analytic process lifecycle
If you're an executive looking to understand the predictive analytics lifecycle, this is a must-read resource and reference guide.
目次
Foreword xi
Preface xiii
Acknowledgments xv
Chapter 1 Problem Identification and Definition 1
Importance of Clear Business Objectives 4
Office Politics 8
Note 13
Chapter 2 Design and Build 15
Managing Phase 16
Planning Phase 18
Delivery Phase 19
Notes 32
Chapter 3 Data Acquisition 33
Data: The Fuel for Analytics 36
A Data Scientist's Job 41
Notes 53
Chapter 4 Exploration and Reporting 55
Visualization 57
Cloud Reporting 61
Chapter 5 Modeling 69
Churn Model 71
Risk Scoring Model 77
Notes 99
Chapter 6 Actionable Analytics 101
Digital Asset Management 104
Social Media 104
Chapter 7 Feedback 129
What the Different Software Components Should Do 132
Note 148
Conclusion 149
Appendix: Useful Questions 155
Bibliography 209
About the Author 211
Index 213
「Nielsen BookData」 より
