Facilitating team-based data science: lessons learned from the DSC-WAV project

TL;DR

This paper discusses the DSC-WAV program, an NSF-funded initiative that engaged undergraduate students in team-based data science projects with non-profit organizations, emphasizing collaboration, skill development, and educational insights.

Contribution

It introduces a team-based data science educational model, detailing processes, tools, and evaluation results to enhance student engagement and skills in real-world contexts.

Findings

Students reported increased confidence in technical and non-technical skills.

The project successfully engaged students in collaborative data science work.

Insights for improving team-based data science education are provided.

Abstract

While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their technical and non-technical data science skills, the project promoted a team-based approach to data science, adopting several processes and tools intended to facilitate this collaboration. Evidence from the project evaluation, including participant survey and interview data, is presented to document the degree to which the project was successful in engaging students in team-based data science, and how the project changed the students' perceptions of their technical and non-technical skills. We also examine opportunities for improvement and offer insight to other data science educators who may want to implement a similar team-based approach to data science projects at their own institutions.