The DataSquad Experiment: Lessons for Preparing Data and Computer Scientists for Work

TL;DR

DataSquad at Carleton College addresses the data-services gap in small liberal arts settings by deploying a work-study, peer-mentored, project-based model that teaches FAIR data practices and open science. The paper details the model’s roles, governance, and assessment framework, and reports on positive alumni outcomes and high client satisfaction. Analyses show strong development in communication and teamwork—key drivers of career influence—alongside meaningful technical gains, with a robust correlation between skill growth and post-college impact ($r=0.78$, $p=0.024$). The findings support broader adoption and adaptation of DataSquad-like programs to build early-career readiness for research software engineering and data-enabled work.

Abstract

The DataSquad at Carleton College addresses a common problem at small liberal arts colleges: limited capacity for data services and few opportunities for students to gain practical experience with data and software development. Academic Technologist Paula Lackie designed the program as a work-study position that trains undergraduates through structured peer mentorship and real client projects. Students tackle data problems of increasing complexity-from basic data analysis to software development-while learning FAIR data principles and open science practices. The model's core components (peer mentorship structure, project-based learning, and communication training) make it adaptable to other institutions. UCLA and other colleges have adopted the model using openly shared materials through "DataSquad International." This paper describes the program's implementation at Carleton College and examines how structured peer mentorship can simultaneously improve institutional data services and provide students with professional skills and confidence.

Figures (6)

• Figure 1: Recommended areas to focus DataSquad efforts, skill influence in post-college success, and skill development while on the DataSquad for alumni. Percent frequency in recommended skill areas is frequency of skill area being placed in top 4. Skill influence and development ratings are averaged across responses.
• Figure 2: Self-assessed skill development during their time as members of the DataSquad. Responses are broken down by the most recent post-graduate position. Jittered point colors reflect the self-assessed importance of each skill in their post-graduate work.
• Figure 3: Thematic categories in alumni open-ended responses. Height of light green bars represents prevalence of main categories (count out of 18 total responses). Internal dark green bars show frequency of subcategories. For a single response, neither main categories nor subcategories are mutually exclusive.
• Figure 4: Recommendation likelihood and satisfaction ratings among DataSquad clients. Likelihood to recommend the DataSquad is expressed in a 0-10 scale while satisfaction ratings are between 0 and 5.
• Figure 5: Alumni assessment of the DataSquad environment.