The Data Mine model for accessible partnerships in data science

Journal Article · 20 February 2024 · Wiley Interdisciplinary Reviews (WIREs) Computational Statistics

DOI: https://doi.org/10.1002/wics.1642 · OSTI ID:2311924

Betz, Margaret A.  ^[1]; Sharples, Rebecca L.  ^[2]; Ward, Mark Daniel ^[1]

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1. The Data Mine Purdue University West Lafayette Indiana USA
2. West Lafayette Indiana USA

Abstract The Data Mine at Purdue University is a pioneering experiential learning community for undergraduate and graduate students of any background to learn data science. The first data‐intensive experience embedded in a large learning community, The Data Mine had nearly 1300 students in academic year (AY) 2022–2023 and nearly 1700 students for AY 2023–2024. The Data Mine embodies data‐infused education, research, and collaboration. Students learn Python, R, SQL, and shell‐scripting, while working on weekly projects within a high‐performance computing (HPC) cluster. In the Corporate Partners cohort, students work on teams of 5–15 students, led by a paid student team leader. Each cohort follows an Agile approach, working on data‐intensive projects provided by industry partners and mentored by company employees. Students develop professional and data skills throughout the academic year, from August through April. Many students return in subsequent years to the program, increasing their tenure with a Corporate Partner. Student teams are inherently interdisciplinary; students from 133 different majors are involved in the program, ranging from new incoming students through PhD level students. These interdisciplinary teams of students bring new perspectives to challenging problems in which data science is a key part of the solution. The interdisciplinary teams foster an environment of synthesis with ideas and solutions. Students come together with different life experiences, different levels of technical skill, but also varying ways they navigate paths to solutions because of the variety of majors represented, resulting in a more creative and robust solution than a traditional data science program. This article is categorized under: Applications of Computational Statistics > Education in Computational Statistics

• Sponsoring Organization: USDOE
• OSTI ID: 2311924
• Journal Information: Wiley Interdisciplinary Reviews (WIREs) Computational Statistics, Journal Name: Wiley Interdisciplinary Reviews (WIREs) Computational Statistics Journal Issue: 1 Vol. 16; ISSN 1939-5108
• Publisher: Wiley Blackwell (John Wiley & Sons)Copyright Statement
• Country of Publication: United States
• Language: English