Sean Kross
ABSTRACT
Data science has been growing in prominence across both academia and industry, but there is still little formal consensus about how to teach it. Many people who currently teach data science are practitioners such as computational researchers in academia or data scientists in industry. To understand how these practitioner-instructors pass their knowledge onto novices and how that contrasts with teaching more traditional forms of programming, we interviewed 20 data scientists who teach in settings ranging from small-group workshops to large online courses. We found that: 1) they must empathize with a diverse array of student backgrounds and expectations, 2) they teach technical workflows that integrate authentic practices surrounding code, data, and communication, 3) they face challenges involving authenticity versus abstraction in software setup, finding and curating pedagogically-relevant datasets, and acclimating students to live with uncertainty in data analysis. These findings can point the way toward better tools for data science education and help bring data literacy to more people around the world.
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Index Terms
- Practitioners Teaching Data Science in Industry and Academia: Expectations, Workflows, and Challenges
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