The Data Mine: Enabling Data Science Across the Curriculum

Gundlach, Ellen; Ward, Mark Daniel

doi:10.1080/10691898.2020.1848484

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…See, for instance, Margolis and Fisher (2002) which also considers, through a four‐year longitudinal study, the impact of gender and race on persistence in the computational sciences. Gundlach and Ward (2021) and Ward (2015) also emphasize the benefits of learning communities in Statistics. Caviglia‐Harris (2022) examines the positive effect of living learning communities on undergraduate student retention and GPA.…”

Section: About the Data Minementioning

confidence: 99%

The Data Mine model for accessible partnerships in data science

Betz,

Sharples,

Ward

2024

WIREs Computational Stats

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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

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Section: About the Data Minementioning

confidence: 99%

The Data Mine model for accessible partnerships in data science

Betz,

Sharples,

Ward

2024

WIREs Computational Stats

Self Cite

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“…A less technical approach is described by Burckhardt et al (2021) using the suite of materials implemented by their Integrated Statistics Learning Environment (ISLE). An immersive data science living and learning community is presented by Gundlach & Ward (2021). Finally, Theobold et al (2021) describe an alternative to course learning through a series of workshops.…”

Section: Creative Structuresmentioning

confidence: 99%

Integrating computing in the statistics and data science curriculum: Creative structures, novel skills and habits, and ways to teach computational thinking

Horton¹,

Hardin²

2020

Preprint

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Nolan and Temple Lang (2010) argued for the fundamental role of computing in the statistics curriculum. In the intervening decade the statistics education community has acknowledged that computational skills are as important to statistics and data science practice as mathematics. There remains a notable gap, however, between our intentions and our actions. In this special issue of the Journal of Statistics and Data Science Education we have assembled a collection of papers that (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking. We believe that it is critical for the community to redouble our efforts to embrace sophisticated computing in the statistics and data science curriculum. We hope that these papers provide useful guidance for the community to move these efforts forward.

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“…The National Academies of Science, Engineering, and Medicine emphasize the interdisciplinary nature of data science and point out that researchers from all backgrounds should have opportunities to learn data science at all levels (Gundlach and Ward, 2020). This A-Z guide aims to address four constraints in inclusive use of sampling and census science as follows:…”

Section: Introductionmentioning

confidence: 99%