Philip Guo (Phil Guo, Philip J. Guo, Philip Jia Guo, pgbovine)

Practitioners Teaching Data Science in Industry and Academia: Expectations, Workflows, and Challenges

research paper summary
Practitioners Teaching Data Science in Industry and Academia: Expectations, Workflows, and Challenges. Sean Kross and Philip J. Guo. ACM Conference on Human Factors in Computing Systems (CHI), 2019.
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.
@inproceedings{KrossCHI2019,
 author = {Kross, Sean and Guo, Philip J.},
 title = {Practitioners Teaching Data Science in Industry and Academia: Expectations, Workflows, and Challenges},
 booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems},
 series = {CHI '19},
 year = {2019},
}

(summary written by Sean Kross)

Last year I had the privilege of talking to twenty data scientists who also teach their craft in a variety of settings: big and small courses, online and in person, and in academia and industry. They graciously told me about how they approach the challenge of teaching such a broad and rapidly-changing field. Data science now encompasses an overwhelming number of technologies and techniques including modeling, visualization, data wrangling, storytelling, version control, and system administration. How can they possibly keep a handle on everything? [...]

read the rest of this summary on Sean's webpage

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