Date(s) - 11/09/2016
2:30 pm - 3:30 pm


3910 15th Ave NE
Seattle WA

The next Data Science Studies meeting will be Wednesday, November 9th from 2:30 to 3:30 p.m. in the WRF Data Science Studio (6th floor of the Physics and Astronomy Tower).

The session is being organized by Jaime Snyder of the UW iSchool, who will lead us in a discussion of data science and pedagogy, with a focus on challenges and opportunities related to providing students with a human-centered perspective on data science. We’ll be joined by a number of guest speakers with experience teaching data science methods and studying the pedagogy of data science:

  • Bill Howe (University of Washington) will provide a brief history of data science pedagogy at UW and will serve as a discussant.
  • Jevin West (University of Washington) will cover emerging approaches to data science curricula and the current tendency toward informal learning in data science.
  • Amelia Acker (University of Texas at Austin) will discuss informal/connected learning in public libraries, and how that relates to data literacy and vernacular data science in the communities that she works.
  • Katie Shilton (University of Maryland) will present on an emerging project with Jaime Snyder analyzing data science curricula to understand the norms, values, and approaches to ethics being taught to data scientists through formal degree programs.

To lay the groundwork for the talk, it would be great if everyone could read the chapter below recently written by Jevin West, who will be the first presenter at the session. Jevin points out that this piece is not specifically about data science pedagogy, but we hope that the section on informal teaching settings will provoke conversation. In fact, there is little, if anything, on pedagogy for this nascent field. Because the field is so young, informal settings (bootcamps, hackathons, online content, etc) are filling many current teaching needs. During this seminar session we will discuss what can we learn from these informal teaching settings in data science and how can this be incorporated into more formal, traditional teaching settings?

Looking forward to a great discussion!

Click to read the chapter: west_data_goldrush