Principles and Foundations of Data Science

M. Tamer Özsu · David R. Cheriton School of Computer Science, University of Waterloo

This is the companion site for Principles and Foundations of Data Science. It hosts material that supplements the book and is updated between printings.

Contents

• Errata — corrections, in page order, with the printing each applies to.
• Review Questions — per-chapter questions for self-study and for instructors assembling problem sets.
• Slides — per-chapter lecture slides, released as they are prepared.

About the book

The book argues that data science rests on four co-equal pillars — data engineering, data analytics, data protection, and ethics — and that protection and ethics are constitutive of the field rather than appendices to it. Around those pillars it develops the technical foundations the pillars draw on, the social and policy context in which data science operates, the lifecycle through which projects move, and the applications and system architectures in which the whole is realized.

The fourteen chapters are organized into five parts:

Part	Chapters
(Opening)	1. Introduction · 2. What is Data Science?
I. Technical Foundations	3. Computing · 4. Mathematics and Optimization · 5. Statistics and Machine Learning
II. The Four Pillars	6. Data Engineering · 7. Data Analytics · 8. Data Protection · 9. Data Science Ethics
III. Context and Process	10. The Social and Policy Context · 11. Data Science Lifecycle
IV. Practice	12. Applications — Examples · 13. System Architecture
V. Synthesis and Outlook	14. Conclusions

Contact

Corrections and comments: tamer.ozsu@uwaterloo.ca, or open an issue.