The Data Science Education Podcast · Berkeley Data Science

Utilizing Data Science for Social Good and Interdisciplinary Research (feat. Sarah Stone)

April 7, 2023·27 min·2 clips

Eric Van Dusen and Kelechi Nebedom kick off UC Berkeley's podcast on shaping the future of data science education.

This episode of The Data Science Education Podcast features hosts Eric Van Dusen and Kelechi Nebedom from UC Berkeley in conversation with Sarah Stone, Executive Director of the University of Washington's eScience Institute. They explore the institute's role as a campus hub for interdisciplinary data science work. Sarah Stone details the eScience Institute's founding in 2014 and its mission to create a neutral space for cross-disciplinary collaboration. The institute focuses on identifying common methodological challenges and shared interests in developing data science tools across different fields. A key initiative discussed is the "Data Science for Social Good" program, which partners students with public and nonprofit organizations. These projects apply data science to address societal challenges, providing students with practical, real-world experience. Stone emphasizes the importance of these partnerships for both student learning and community impact. She explains that the institute's structure is designed to break down academic silos and foster collaboration between domain experts and data scientists. The conversation highlights how this model accelerates research and innovation by combining diverse expertise. A memorable insight is the institute's role in cultivating a shared language and methodology for data-intensive research across campus. Stone describes the long-term vision of embedding data science as a fundamental, collaborative component in all research disciplines. The tone is informal, conversational, and educational, focused on sharing institutional models and pedagogical strategies. Listeners interested in academic program development, interdisciplinary research, or data science education will find this episode valuable. Those seeking technical deep dives or critiques of specific data science methods might find the discussion too high-level.

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