About the Roundtable
The Roundtable on Data Science Postsecondary Education brings together representatives from academic data science programs, funding agencies, professional societies, foundations, and industry to discuss the community’s needs, best practices, and ways to move forward. The roundtable will help affected communities develop a coherent and shared view of the emerging field of data science and of how best to prepare large numbers of professionals to help realize the potential of this field.
The roundtable convenes four meetings per year. Each meeting focuses on a topic related to data science education or practice, and consists of presentations from experts followed by open discussions of the roundtable. All meetings are open to the public and advertised to the broader data science community. Meetings will be webcast live with the capability for remote participation, and all videos and slides from each meeting will be posted online. Meeting highlights will be produced following each meeting to summarize the presentations and discussions that occurred.
The roundtable is sponsored by the Gordon and Betty Moore Foundation, the National Institutes of Health, the National Academy of Sciences W. K. Kellogg Foundation Fund, the Association for Computing Machinery, the American Statistical Association, and the Mathematical Association of America.
December 10, 2018 Motivating Data Science Education through Social Good
September 17, 2018 Challenges and Opportunities to Better Engage Women and Minorities in Data Science Education
June 13, 2018 Programs and Approaches for Data Science Education at the PhD Level
March 23, 2018 Improving Reproducibility by Teaching Data Science as a Scientific Process
December 8, 2017 Integrating Ethical and Privacy Concerns into Data Science Education
October 20, 2017 Alternative Mechanisms for Data Science Education
May 1, 2017 Data Science Education in the Workplace
March 20, 2017 Examining the Intersection of Domain Expertise and Data Science
December 14, 2016 The Foundations of Data Science from Statistics, Computer Science, Mathematics, and Engineering