Data Science in Context:
Foundations, Challenges, Opportunities

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

Preface
Acknowledgments
Introduction
Part I. Data Science
Chapter 1. Foundations of Data Science
Chapter 2. Data Science is Transdisciplinary
Chapter 3. A Framework for Ethical Considerations
Part II. Applying Data Science
Chapter 4. Data Science Applications: Six Examples
Chapter 5. The Analysis Rubric
Chapter 6. Applying the Analysis Rubric to More Use Cases
Chapter 7. A Principlist Approach to Ethical Considerations
Part III. Challenges in Applying Data Science
Chapter 8. Tractable Data
Chapter 9. Building and Deploying Models
Chapter 10. Dependability (Privacy, Security, Abuse-Resistance, Resilience)
Chapter 11. Understandability
Chapter 12. Setting the Right Objectives
Chapter 13. Toleration of Failures
Chapter 14. Ethical, Legal, and Societal Challenges
Part IV. Addressing Concerns
Chapter 15. Societal Concerns
Chapter 16. Education and Intelligent Discourse
Chapter 17. Regulation
Chapter 18. Research and Development
Chapter 19 Quality and Ethical Governance
Chapter 20. Concluding Thoughts
Appendix 1 Summary of Recommendations from Part IV
References
About the Authors