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 approachto data science projects at their own institutions.
Bibliographical noteFunding Information:
Acknowledgments. We acknowledge the support of NSF grants HDR DSC-1923388, HDR DSC-1923700, HDR DSC-1923934, and HDR DSC-1924017. We also want to acknowledge the community organizations that participated in the project and Andrea Dustin, whose organization and faculty-herding skills have kept this project moving forward.
In this paper, we describe the Data Science Corps: Wrangle-Analyze-Visualize (DSC-WAV) program, a data science workforce development project funded by the NSF as part of the Harnessing the Data Revolution (HDR) initiative. In the DSC-WAV program, teams of undergraduate students work with a local non-profit organization on a data-focused problem. To facilitate a team-based approach to data science, the project adopted an Agile framework, code review, and collaborative management and coding tools (e.g., Git, GitHub, Trello). We present evidence from the project evaluation, including data from surveys and interviews with students and faculty, documenting the projects’ successes in utilizing this approach, as well as opportunities for improvement. In particular, we examine how engaging in this team-based data science project helped develop the participants’ understanding of their technical and non-technical skills (e.g., interacting with clients), as well as their sense of agency and empowerment for “doing” data science.
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- Projectbased learning.
- Skill development
- Team-based learning