A project-based case study of data science education

Abstract

The discipline of data science has emerged over the past decade as a convergence of high-power computing, data visualization and analysis, and data-driven application domains. Prominent research institutions and private sector industry have embraced data science, but foundations for effective tertiary-level data science education remain absent. This is nothing new, however, as the university has an established tradition of developing its educational mission hand-in-hand with the development of novel methods for human understanding (Feingold, 1991). Thus, it is natural that universities "figure out" data science concurrent with the development of needed pedagogy. We consider data science education with respect to recent trends in interdisciplinary and experiential educational methodologies. The first iteration of the Berkeley Institute for Data Science (BIDS) Collaborative, which took place at the University of California, Berkeley in the Spring of 2015, is used as a case study. From this, we draw lessons learned regarding the necessary components of effective tertiary data science education, which range from a complete end-to-end workflow, technological tools for development and team communications, and appropriate motivation and incentives. Our findings will be tested and revised in subsequent iterations of the BIDS Collaborative as we continue our study of data science education, research, and social impact.