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

Data Science in Context (eBook, PDF)

Foundations, Challenges, Opportunities

• Format: PDF

Produktbeschreibung

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

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 20. Oktober 2022
• Englisch
• ISBN-13: 9781009272216
• Artikelnr.: 70911401

Autorenporträt

Alfred Z. Spector is a technologist and research leader. His career has led him from innovation in large scale, networked computing systems (at Stanford, CMU, and his company, Transarc) to broad research leadership: first leading IBM Software Research and then Google Research. Following Google, he was the CTO at Two Sigma Investments, and he is presently a Visiting Scholar at MIT. In addition to his managerial career, Dr. Spector lectured widely on the growing importance of computer science across all disciplines (CS+X) and on the Societal Implications of Data Science. He is a fellow of the ACM, IEEE, and the American Academy of Arts and Sciences, and a member of the National Academy of Engineering. Dr. Spector won the 2001 IEEE Kanai Award for Distributed Computing, was co-awarded the 2016 ACM Software Systems Award, and was a Phi Beta Kappa Visiting Scholar. He received a Ph.D. in Computer Science from Stanford and an A.B. in Applied Mathematics from Harvard.

Inhaltsangabe

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.