Data Science Pedagogy to Support Industry, Governmental, and Research Initiatives
summary
Course runs as a 12-week course with ~40–45 senior undergrads, three milestones (week 5 problem definition, week 8 data collection/cleaning, final report) and weekly client meetings; uses Python, web-scraping, GIS, NLP and an implemented supervised framework to build a knowledge graph interface and other applied projects. Outcome: 15 multidisciplinary projects, instructor-led refinement toward publication, and three student-led peer‑reviewed papers; requires heavy instructor coordination and modest conference funding.
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chapters (7)
- 0:00Data science pedagogy intro
- 2:34Motivation and methodology
- 6:11Course structure and projects
- 9:24Student project outcomes
- 13:12Lessons for educators
- 17:49Funding challenges
- 20:22Q&A
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