Emancipatory Data Science Education: A Framework for Liberation, Inclusion, and Affirmation

• In Event: Curriculum, Community, and Inter/disciplinarity: Dismantling Dominant Curricular Approaches

Abstract

Despite the rapid growth in data analytics talent, the supply of data workers is not expected to keep up with growing industry demand. This creates an opportunity for non-STEM students to expand career opportunities, particularly within data science sub-fields (i.e., data visualization or data journalism). Given that the majority of Black, Indigenous and People of Color (BIPOC) students pursue non-STEM majors this population represents a large untapped resource for the future DS workforce. This paper 1) proposes a conceptual framework to 1) empower existing BIPOC data science students; 2) train and highlight asset and strengths-based approaches to BIPOC data science education and 3) expand the pool of future data science educators to reflect the increasingly diverse U.S. population.

Author

• Thema Monroe-White, Berry College