Data Science in Context
Foundations, Challenges, Opportunities

Four leading experts convey the promise of data science and examine challenges in achieving its benefits and mitigating some harms.

Alfred Z. Spector (Author), Peter Norvig (Author), Chris Wiggins (Author), Jeannette M. Wing (Author)

9781009272209, Cambridge University Press

Hardback, published 20 October 2022

335 pages
25 x 17.4 x 2.2 cm, 0.72 kg

'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

Data 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.

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.

Subject Areas: Mathematical theory of computation [UYA], Databases [UN], Algorithms & data structures [UMB], Probability & statistics [PBT], Manufacturing industries [KND], Entrepreneurship [KJH], Data analysis: general [GPH]