Fostering Undergraduate Data Science

Yavuz, Fulya Gokalp; Ward, Mark Daniel

doi:10.1080/00031305.2017.1407360

Cited by 13 publications

(2 citation statements)

References 7 publications

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“…In a 5-year National Science Foundation funded pilot project (2014-2020), which lead to the creation of The Data Mine in 2018, more than 50% of the 100 students were female. In the last year of the pilot, we had 70% female student participants, 20% Black females, and one Deaf female participant [3]. Now that we are operating at a much larger scale of 600+ students, we still strongly emphasize a diverse and inclusive environment.…”

Section: Closing Thoughtsmentioning

confidence: 99%

The next wave: We will all be data scientists

Betz

Gundlach

Hillery

et al. 2020

Statistical Analysis

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In the next wave of educating future data scientists, we need to think of all undergraduate students, regardless of background or major, as future data scientists. We should train them in supportive, interdisciplinary environments. Starting from their first day at college, they should be given the opportunity to apply powerful tools to large data sets, using real-world problems. Partnerships with research computing, academic departments, research centers, companies, government, and nonprofits will all be necessary to fully prepare these students for the breadth of the data science workforce.

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Section: Closing Thoughtsmentioning

confidence: 99%

The next wave: We will all be data scientists

Betz

Gundlach

Hillery

et al. 2020

Statistical Analysis

View full text Add to dashboard Cite

show abstract

“…The data science curriculum for undergraduate students in all disciplines needs to be developed with great attention to promoting the importance of science in society and fostering students' intrinsic motivation to learn science. Yavuz and Ward discussed effective data science learning environments and proposed a unified program built on a foundation of team-oriented, data-driven projects [3].…”

Section: Introduction 11 Backgroundmentioning

confidence: 99%

Improving Motivation in Learning AI for Undergraduate Students by Case Study

Murakami,

Sho,

Inagaki

2024

Journal of Information Processing

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The purpose of introductory data science education for undergraduates is to increase students' interest in data science and motivate them to learn. In Japanese university students' standard data science curriculum, group work based on AI application examples is recommended to motivate students to learn data science. All university students who have just graduated from high school may not have sufficient basic data science knowledge, and their understanding of its importance may differ from person to person. Under these circumstances, it is unclear whether an introduction to AI applications would be practical as an introductory education to data science. Therefore, in this study, we designed a lesson including group work based on a case study of using AI technology in job interviews. Although first-year university students' willingness to learn AI differed by department, we found that their motivation in learning AI increased after the lesson.

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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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Fostering Undergraduate Data Science

Cited by 13 publications

References 7 publications

The next wave: We will all be data scientists

The next wave: We will all be data scientists

Improving Motivation in Learning AI for Undergraduate Students by Case Study

The Data Mine model for accessible partnerships in data science

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