Author(s):
NEED:Scaling new courses and program offerings for data science education and increasing diversity of thought can be challenging in this skill-demand driven ecosystem. Educators are increasingly recognizing the value of building communities of practice and adapting and translating courses and programs that have been shown to be successful and sharing lessons learned in increasing diversity in data science education. GUIDING QUESTIONS:We describe and analyze our experiences translating three components of a lower-division data science curriculum from one university, UC Berkeley, to other settings with very different student populations and institutional contexts, University of Maryland Baltimore County and Mills College. We highlight lessons learned to reflect on the existing large scale programs and what may be opportunities for increasing diversity. OUTCOMES:Our findings emphasize the importance of adapting courses and programs to existing curricula, student populations, cyberinfrastructure, and faculty and staff resources. Smaller class sizes open up the possibility of more individualized assignments, tailored to the majors, career interests, and social change motivations of diverse students. While students across institutional contexts may need varying degrees of support, we found that often students from diverse backgrounds, if engaged deeply, show significant enthusiasm for data science and its applications. BROADER IMPACTS:Our experiences raise critical questions: who do we lose in the process of scaling? How can large programs better serve historically marginalized students? Larger programs may seem alienating and impersonal, but opportunities to become part of a smaller, diverse, and inclusive community of students can create a subculture where students can find mentorship and individualized support.
Coauthors
Vandana Janeja, UMBC, Baltimore, MD; Susan Wang, Mills College, Oakland, CA; David J. Harding, UC Berkeley, Berkeley, CA
