Author(s):
Data science, a rapidly growing STEM field, exhibits persistent low participation rates among women and underrepresented minorities (URMs), including Black and Hispanic individuals.The intersectionality of gendered and racialized dynamics in undergraduate data science education has been rarely studied. This study aims to illuminate gendered and racialized dynamics and their intersectionality in undergraduate data science education. DataThis study looked at the administrative records of college graduates (N = 16,965; SP2020-SP2022), student surveys, and student interviews. Linear Probability Modeling (LPM) was used to examine the covariates (Demographic and Educational). Women of color face heightened difficulties in persisting in undergraduate data science education. Building a campus community that supports marginalized students is essential for improving diversity and inclusion in data science workforce development.
Coauthors
Byeongdon Oh, State University of New York Polytechnic Institute; David Harding, UC Berkeley
