title: "Practitioners Teaching Data Science in Industry and Academia: Expectations, Workflows, and Challenges"
authors: Sean Kross and Philip J. Guo
venue: ACM Conference on Human Factors in Computing Systems (CHI)
year: 2019
footer: "<font color=\"#AA0000\">Honorable Mention Paper</font>"
links:
- <a href="https://seankross.com/2019/01/08/The-Front-Lines-of-Teaching-Data-Science.html">Blog post</a>

tweet: Data science instructors teach authentic workflows & face challenges around software and data setup

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.

bibtex: >
  @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},
   isbn = {978-1-4503-5970-2},
   location = {Glasgow, Scotland Uk},
   pages = {263:1--263:14},
   articleno = {263},
   numpages = {14},
   url = {http://doi.acm.org/10.1145/3290605.3300493},
   doi = {10.1145/3290605.3300493},
   acmid = {3300493},
   publisher = {ACM},
   address = {New York, NY, USA},
   keywords = {data science education, teaching programming},
  }