The Real Work of Data Science

Turning data into information, better decisions, and stronger organizations

Ron S. Kenett, Thomas C. Redman

• Publisher: Wiley
• Year: 2019
• Print ISBN: 9781119570707
• eBook ISBN: 9781119570769
• Edition: 1
• Topic: Biological Sciences
• Subtopic: Statistics for Business & Economics
• Index: Biological Sciences

The essential guide for data scientists and for leaders who must get more from their data science teams

The Economist boldly claims that data are now "the world's most valuable resource." But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. The Real Work of Data Science explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that spells the difference between a good data scientist and a great one, between a team that makes marginal contributions and one that drives the business, between a company that gains some value from its data and one in which data truly is "the most valuable resource."

"These two authors are world-class experts on analytics, data management, and data quality; they've forgotten more about these topics than most of us will ever know. Their book is pragmatic, understandable, and focused on what really counts. If you want to do data science in any capacity, you need to read it."
—Thomas H. Davenport, Distinguished Professor, Babson College and Fellow, MIT Initiative on the Digital Economy

"I like your book. The chapters address problems that have faced statisticians for generations, updated to reflect today's issues, such as computational Big Data."
—Sir David Cox, Warden of Nuffield College and Professor of Statistics, Oxford University

"Data science is critical for competitiveness, for good government, for correct decisions. But what is data science? Kenett and Redman give, by far, the best introduction to the subject I have seen anywhere. They address the critical questions of formulating the right problem, collecting the right data, doing the right analyses, making the right decisions, and measuring the actual impact of the decisions. This book should become required reading in statistics and computer science departments, business schools, analytics institutes and, most importantly, by all business managers."
—A. Blanton Godfrey, Joseph D. Moore Distinguished University Professor, Wilson College of Textiles, North Carolina State University

Table of contents

• Cover
• Table of Contents
• About the Authors
• Preface
• About the Companion Website
• 1 A Higher Calling
• 2 The Difference Between a Good Data Scientist and a Great One
• 3 Learn the Business
• 4 Understand the Real Problem
• 5 Get Out There
• 6 Sorry, but You Can't Trust the Data
• 7 Make It Easy for People to Understand Your Insights
• 8 When the Data Leaves Off and Your Intuition Takes Over
• 9 Take Accountability for Results
• 10 What It Means to Be “Data‐driven”
• 11 Root Out Bias in Decision‐making
• 12 Teach, Teach, Teach
• 13 Evaluating Data Science Outputs More Formally
• 14 Educating Senior Leaders
• 15 Putting Data Science, and Data Scientists, in the Right Spots
• 16 Moving Up the Analytics Maturity Ladder
• 17 The Industrial Revolutions and Data Science
• 18 Epilogue
• Appendix A: Skills of a Data Scientist
• Appendix B: Data Defined
• Appendix C: Questions to Help Evaluate the Outputs of Data Science
• Appendix D: Ethical Considerations and Today's Data Scientist
• Appendix E: Recent Technical Advances in Data Science
• References
• A List of Useful Links
• Index
• End User License Agreement