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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 howthat 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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Use your power for good: Collective action to overcome institutional injustices impeding ethical science communication in the academy
Abstract Science communication (scicomm) shapes our world by helping people use science to make societal and personal decisions. Supporting and doing ethical scicomm requires valuing diverse perspectives and the people who do scicomm. Unfortunately, institutional hurdles ingrained in academia impede and undermine ethical scicomm. The injustices impeding scicomm stem from the prestige paradigm of academia (articulated in the present article), which reinforces hierarchical relationships in an exclusionary and exploitative system. To move academia forward, we name and review these injustices through the lens of five realms of scicomm (scientific communication, teaching scicomm, academics engaging in scicomm, scicomm research, and scicomm careers beyond academia). We then provide a novel framework, helping readers identify axes of influence and how they can leverage their intersectional, academic capital to take concrete action to remove the hurdles impeding ethical scicomm in academia.
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- PAR ID:
- 10547883
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- BioScience
- Volume:
- 74
- Issue:
- 11
- ISSN:
- 0006-3568
- Format(s):
- Medium: X Size: p. 747-769
- Size(s):
- p. 747-769
- Sponsoring Org:
- National Science Foundation
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