Ron S. Kenett - The Real Work of Data Science

1. Chapter 1
2. Chapter 6
3. Chapter 7
4. Chapter 8
5. Chapter 10
6. Chapter 11
7. Chapter 12
8. Chapter 14
9. Chapter 15
10. Chapter 16

TURNING DATA INTO INFORMATION, BETTER DECISIONS, AND STRONGER ORGANIZATIONS

Ron S. Kenett

Ra'anana, Israel

Thomas C. Redman

Rumson, NJ, USA

These two authors are worldclass experts on analytics, data management, and data quality; theyve 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 todays issues, such as computational big data.

Sir David Cox
Warden of Nuffield College and Professor of Statistics, Oxford University

I am already in love with your book based on the overview and preface!! What a creative approach! Speaks a lot to your ability to tell a good story one of the key ways of reasoning for a good data scientist!

Hollylynne S. Lee
Professor, Mathematics and Statistics Education and Faculty Fellow, Friday Institute for Educational Innovation, North Carolina State University

The root causes of business failures typically are management, not technology. In todays complex and changing digital world, the advice in The Real Work of Data Science is essential. Read it and do it.

John A. Zachman
Chairman Zachman International and Executive Director FEAC Institute

If you are wondering what the real challenges and solutions to solving your Big Data problem are, this is a must read book. Ron and Tom move past the technology hype and highlight the real issues and opportunities in leveraging data science to the benefit of your organization

Jeff MacMillan
Chief Analytics and Data Officer, Morgan Stanley Wealth Management

Much needed!

Neil Lawrence
Professor of Machine Learning at the University of Sheffield and Machine Learning team manager at Amazon

More than 80% of data science projects fail, either partially or wholly, at the implementation stage. There is a wealth of books on the technical and mechanical aspects of data science, but little to guide data scientists and managers on the holistic integration of data science into organizations in a way that produces success. This wellwritten book fills that gap.

Peter Bruce
Founder and Chief Academic Officer, The Institute for Statistics Education

Cest livre est trs intressant et plein de trs bonnes choses intelligentes et utiles. Il sera sans nul doute trs prcieux.

Jean Michel Poggi
Professor of Statistics at ParisDescartes University and Mathematics Laboratory, Orsay University, Paris, France,

Past President of the Socit Franaise de Statistique and VicePresident of the Federation of European National Statistical Societies

I like the very direct and succinct style. You are certainly right on target when you say you cant stress enough the importance of understanding the real problem. Other of your points in really hit home, such as data scientists spending more time on data quality than on analysis. (Im glad they do.) Further, you are absolutely correct that data scientists must translate their results into the language of the decisionmaker. I also recognize the liberal use of anecdotes in the book. For instance, the remarks about Bill Hunter, the ice cream sales, the Pokmon experiment, etc. I personally like this, and I do this in all of my speeches since I think it really hooks the audience.

Barry Nussbaum
Past Chief Statistician, the United States Environmental Protection Agency and Past President of the American Statistical Association

I think this book is excellent for an introductory course in data science. It could be used with students at university level or with professionals in specialist courses.

Luciana Dalla Valle
Lecturer in Statistics and Programme Manager of the MSc Data Science and Business Analytics, School of Computing, Electronics and Mathematics, Plymouth University, UK

The Real Work of Data Science addresses the softer issues of data science that actually decide on the success or failure of any data science initiative. It makes the data science and Chief Analytics Officer roles more understandable and accessible to a wider audience. Choosing the right modeling method is often the key point of discussion in books, although it is just a tiny fraction of the job to be done. This book prepares you for the harsh reality of data science in the realworld.

Alexander Borek
Global Head of Data & Analytics at Volkswagen Financial Services

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

This edition first published 2019
2019 Ron S. Kenett and Thomas C. Redman

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