Document Type
Article
Publication Date
10-21-2021
Publication Title
Statistical and Data Sciences: Faculty Publications
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.
Recommended Citation
Legacy, Chelsey; Zieffler, Andrew; Baumer, Benjamin S.; Barr, Valerie; and Horton, Nicholas J., "Facilitating Team-Based Data Science: Lessons Learned from the DSC-WAV Project" (2021). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/46
Digital Object Identifier (DOI)
doi.org/10.48550/arXiv.2106.11209
Rights
© The Authors Licensed to Smith College and distributed CC-BY under the Smith College Faculty Open Access Policy.
Included in
Data Science Commons, Other Computer Sciences Commons, Statistics and Probability Commons
Comments
Peer reviewed accepted manuscript.