Data Science in Context
Data Science in Context
Peter Norvig, Stanford University, California
Chris Wiggins, Columbia University, New York
Jeannette M. Wing, Columbia University, New York
$39.99
USDData science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world.
- Introduces a rubric that practitioners and students can apply to tease out those complexities when applying data science to new problems
- Ethics is woven into the discussion throughout the book
- Presents extended examples of data science applications from the domains of technology, commerce, science, health, public policy, and many others
Reviews & endorsements
'This book provides an important view of the contextual landscape for data science: the context of related fields of statistics, visualization, optimization, and computer science; the context of a broad range of applications, together with an analysis rubric; the context of societal impacts from dependability, to understandability, to ethical and legal questions. These are critically important factors for any practitioner of data science to understand, and for others to be aware of in evaluating the use of data science.' Daniel Huttenlocher, Massachusetts Institute of Technology
'As data science becomes a crucial element in momentous decisions of war and peace, as well as commerce and innovation, it is vital that it rests on sound foundations. This book is an important step forward in that regard, illuminating the context in which data science is practiced. It is essential reading for both data scientists and decision makers.' James Arroyo, Ditchley Foundation
'Data science touches every aspect of our modern lives. This book digs into the practical, legal, and ethical challenges that result. It is the only book that's comprehensive in its consideration of the thorny issues arising from the broad application and unprecedented growth of data science. If you do data science, you should read this book.' Michael D. Smith, Harvard University
'This book will be essential reading for all data scientists and data teams. The self-contained text explains what students and practitioners need to know to use data science more effectively and ethically. It draws on the authors' years of experience and offers practical insights into data science that complement other books that focus on specific techniques. I'll be referencing and recommending this book for many years to come.' Ben Lorica, Gradient Flow
Product details
October 2022Adobe eBook Reader
9781009272216
0 pages
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- Introduction
- Part I. Data Science:
- 1. Foundations of data science
- 2. Data science is transdisciplinary
- 3. A framework for ethical considerations
- Recap of Part I – Data Science
- Part II. Applying Data Science:
- 4. Data science applications: six examples
- 5. The analysis rubric
- 6. Applying the analysis rubric
- 7. A principlist approach to ethical considerations
- Recap of Part II – Transitioning from Examples and Learnings to Challenges
- Part III. Challenges in Applying Data Science:
- 8. Tractable data
- 9. Building and deploying models
- 10. Dependability
- 11. Understandability
- 12. Setting the right objectives
- 13. Toleration of failures
- 14. Ethical, legal, and societal challenges
- Recap of Part III – Challenges in Applying Data Science
- Part IV. Addressing Concerns:
- 15. Societal concerns
- 16. Education and intelligent discourse
- 17. Regulation
- 18. Research and development
- 19. Quality and ethical governance
- Recap of Part IV – Addressing Concerns:
- 20. Concluding thoughts
- Appendix. Summary of recommendations from Part IV
- About the authors
- References
- Index.
