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

  • * Corresponding author: Chelsey Legacy

    * Corresponding author: Chelsey Legacy 
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  • 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.

    Mathematics Subject Classification: Primary: 97K80; Secondary: 97P99.

    Citation:

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  • Figure 1.  A sample Kanban board used by a DSC-WAV team. This board is implemented through GitHub Projects. Each task is represented by a "card" and linked to an issue in the GitHub repository. As students work on tasks, they move them from "To do" (i.e., the sprint backlog), to "In progress, " to "Done." A quick look at the board helps everyone on the team understand who is working on what. This team used tags to estimate the length of time that each task will take to complete

    Figure 2.  Sample timeline for Cohort 1 that was distributed to teams. A similar schedule was used for later cohorts

    Table 1.  Number of participants (and percentages) from each DSC-WAV team that responded to the end-of-project survey or were interviewed

    Survey Interview
    Team $ N $ (%) $ N $ (%)
    Cohort 1 (Spring 2020)
    [1ex] Team 01 ($ N=5 $) —— 4 (80%)
    Team 02 ($ N=5 $) —— 4 (80%)
    Team 03 ($ N=5 $) —— 3 (60%)
    Team 04 ($ N=5 $) —— 5 (100%)$ ^{\dagger} $
    Team 05 ($ N=5 $) —— 5 (100%)
    Cohort 2 (Fall 2020)
    [1ex] Team 06 ($ N=4 $) 4 (100%) ——
    Team 07 ($ N=3 $) 1 (33%) ——
    Team 08 ($ N=4 $) 3 (75%) ——
    Team 09 ($ N=3 $) 1 (33%) ——
    Team 10 ($ N=8 $)$ ^{\dagger\dagger} $ 4 (50%) ——
    Team 11 ($ N=2 $) 0 (0%) ——
    Cohort 3 (Spring 2021)
    [1ex] Team 12 ($ N=5 $) 1 (20%) 3 (60%)
    Team 13 ($ N=5 $) 0 (0%) 3 (60%)
    Team 14 ($ N=5 $) 0 (0%) ——
    Team 15 ($ N=4 $) 3 (75%) ——
    $ ^\dagger $One participant emailed their interview responses.
    $ ^{\dagger\dagger} $Three participants dropped out mid-project.
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