Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.

Table of Contents


Front Matter
1 Introduction
2 Meeting #1: The Foundations of Data Science from Statistics, Computer Science, Mathematics, and Engineering
3 Meeting #2: Examining the Intersection of Domain Expertise and Data Science
4 Meeting #3: Data Science Education in the Workplace
5 Meeting #4: Alternative Mechanisms for Data Science Education
6 Meeting #5: Integrating Ethical and Privacy Concerns into Data Science Education
7 Meeting #6: Improving Reproducibility by Teaching Data Science as a Scientific Process
8 Meeting #7: Programs and Approaches for Data Science Education at the Ph.D. Level
9 Meeting #8: Challenges and Opportunities to Better Engage Women and Minorities in Data Science Education
10 Meeting #9: Motivating Data Science Education Through Social Good
11 Meeting #10: Improving Coordination Between Academia and Industry
12 Meeting #11: Data Science Education at Two-Year Colleges
References
Appendixes
Appendix A: Biographical Sketches of Roundtable Members
Appendix B: Meeting Participants