Data Science in Context Foundations, Challenges, Opportunities

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.