Four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating potential harms. Aimed at practitioners and students as well as humanists, social scientists, scientists, and policy makers, the book discusses how to use data science more effectively and more ethically.
Four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating potential harms. Aimed at practitioners and students as well as humanists, social scientists, scientists, and policy makers, the book discusses how to use data science more effectively and more ethically.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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