4H: Facilitating Team-Based Data Science: Lessons Learned from the DSC-WAV Project

Benjamin S. Baumer (Smith College), Nicholas J. Horton (Amherst College), Andrew Zieffler (University of Minnesota), & Chelsey Legacy (University of Minnesota)


Data science is a collaborative, interdisciplinary field. When our students enter the workforce, they may find themselves collaborating with co-workers with diverse skill sets, backgrounds, experiences, and now even geographic locations. Data scientists can borrow ideas for collaborative frameworks like Agile, Scrum, and Kanban from the mature field of software development, but how well do these concepts translate into an undergraduate setting?

In this session, we describe an NSF-funded data science workforce development project DSC-WAV program in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To facilitate a team-based approach to data science, the project adapts elements of an Agile framework. Participants will engage in a series of interactive activities introducing elements of Agile, various project management tools, and code review. The session will also include discussion and lessons learned from student collaboration in the DSC-WAV project.