Cross-disciplinary faculty development in data science principles for classroom integration

Abstract

Data science in practice leverages the expertise in computer science, mathematics and statistics with applications in any field using data. The formalization of data science educational and pedagogical strategic remain in their infancy. College faculty from various disciplines are tasked with designing and delivering data science instruction without the formal knowledge of how data science principles are executed in practice. We call this the data science instruction gap. Also, these faculties are implementing their discipline's standard pedagogical strategies to their understanding of data science. In this paper, we present our cross-disciplinary instructional program model designed to narrow the data science instruction gap for faculty. It is designed to scaffold college faculties' data science learning to support their discipline-specific data science instruction. We provide individualized and group-based support structures to instill data science principles and transition them from learners to educators in data science. Lastly, we share our model's impact on and value to faculty as well as make recommendations for model adoption.