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 ContentsFront Matter1 Introduction2 Meeting #1: The Foundations of Data Science from Statistics, Computer Science, Mathematics, and Engineering3 Meeting #2: Examining the Intersection of Domain Expertise and Data Science4 Meeting #3: Data Science Education in the Workplace5 Meeting #4: Alternative Mechanisms for Data Science Education6 Meeting #5: Integrating Ethical and Privacy Concerns into Data Science Education7 Meeting #6: Improving Reproducibility by Teaching Data Science as a Scientific Process8 Meeting #7: Programs and Approaches for Data Science Education at the Ph.D. Level9 Meeting #8: Challenges and Opportunities to Better Engage Women and Minorities in Data Science Education10 Meeting #9: Motivating Data Science Education Through Social Good11 Meeting #10: Improving Coordination Between Academia and Industry12 Meeting #11: Data Science Education at Two-Year CollegesReferencesAppendixesAppendix A: Biographical Sketches of Roundtable MembersAppendix B: Meeting Participants
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