Abstract:
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The (post-secondary) data science education round table (DSERT) was charged by the National Academies of Sciences, Engineering, and Medicine with highlighting the challenges in data science education and the emerging best practices for meeting those challenges. Convening quarterly between 2016 and late 2019, representatives from academia, government, and industry gathered with invited speakers from across the nation to discuss various topics under this charge. This forum was unique in providing an opportunity for representatives from different sectors to explore what academia can offer, how different disciplines can benefit and what industry and government need. The session will start with an executive summary of the discussions held under this round table, on topics that included foundations and domain areas, alternative mechanisms, ethics and privacy, reproducibility, diversity, academia-industry coordination, social good, and graduate program development. This will be followed by a period of open comments from individual panelists on these and related issues, and then open Q&A with the audience, particularly with an eye towards statistics in the broader data science education landscape.
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