A Functional Approach to Data Science in CS1

Albright, Sarah Dahlby; Klinge, Titus H.; Rebelsky, Samuel A.

doi:10.1145/3159450.3159550

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…Kandel et al found great variation in levels of programming ability amongst data scientists [41]. Many of them write code in languages such as Python and R [21,25,35,37], but they are not professional software engineers; moreover, many do not even have formal training in computer science. Much of data scientists' coding activities can be considered end-user programming [46] since they often write code for themselves as a means to gain insights from data rather than intending to produce reusable software artifacts.…”

Section: Related Workmentioning

confidence: 99%

“…These papers fall into two categories: descriptions of courses taught by computer science (CS) faculty, and those taught by faculty in other disciplines. CS faculty have written about their experiences teaching data science both to enrich introductory computing courses with data-oriented applications [16,17,25,34] and in courses intended to serve non-CS-majors [14,20,61]. And faculty in fields ranging from bioinformatics [71], business [21], and statistics [35,37] have written field guides on teaching data science in their respective majors.…”

Section: Teaching Data Sciencementioning

confidence: 99%

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Practitioners Teaching Data Science in Industry and Academia

Kross

Guo

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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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. CCS CONCEPTS • Social and professional topics → Computing education.

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