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Universities have been expanding undergraduate data science programs. Involving graduate students in these new opportunities can foster their growth as data science educators. We describe two programs that employ a near-peer mentoring structure, in which graduate students mentor undergraduates, to (a) strengthen their teaching and mentoring skills and (b) provide research and learning experiences for undergraduates from diverse backgrounds. In the Data Science for Social Good program, undergraduate participants work in teams to tackle a data science project with social impact. Graduate mentors guide project work and provide just-in-time teaching and feedback. The Stanford Mentoring in Data Science course offers training in effective and inclusive mentorship strategies. In an experiential learning framework, enrolled graduate students are paired with undergraduate students from non-R1 schools, whom they mentor through weekly one-on-one remote meetings. In end-of-program surveys, mentors reported growth through both programs. Drawing from these experiences, we developed a self-paced mentor training guide, which engages teaching, mentoring and project management abilities. These initiatives and the shared materials can serve as prototypes of future programs that cultivate mutual growth of both undergraduate and graduate students in a high-touch, inclusive, and encouraging environment.
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Unleashing the Problem-Solving Potential of Next-Generation Data Scientists
Data science, an emerging multidisciplinary field, resides at the intersec- tion of computational sciences, statistical modeling, and domain-specific sciences. The current norm for data science education predominantly focuses on graduate programs, which presume a pre-existing knowledge base in one or more relevant sciences. However, this framework often overlooks those who don’t plan to pursue graduate studies, thereby limiting their exposure to this rapidly expanding field. Penn State addressed this gap by establishing one of the first undergraduate degree programs in Data Sciences, a collaboration between the College of Information Sci- ences and Technology, the Department of Computer Science and Engineering, and the Department of Statistics. One key component of this program is a project-focused, writing-intensive course designed for upper-class undergraduates. This course guides students through the entire data science project pipeline, from problem identifica- tion to solution presentation. It allows students to apply foundational data science principles to real-world problems, advancing their understanding through practi- cal application. This chapter details the objectives, rationale, and course design, alongside reflections from our teaching experience. The insights provided could be helpful to instructors developing similar data science programs or courses at an undergraduate level, broadening the influence of this important field
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- PAR ID:
- 10629569
- Editor(s):
- Carroll, John M
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- ISBN:
- 978-3-031-61289-3
- Page Range / eLocation ID:
- 87 to 110
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Book Chapter:
- https://doi.org/10.1007/978-3-031-61290-9_8
