QMNet Talk: Data Science: Meta-Expertise or Interstitial Craft?

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mcds@unimelb.edu.au

Melbourne Centre for Data Science is proud to support the University of Melbourne Quantitative Research Methods Network (QMNET). The 2021 QMNET seminar series will run as weekly talks on a variety of topics that span disciplinary boundaries.

The next QMNet talk is presented by Anissa Tanweer is a research scientist at the University of Washington's eScience Institute. As an ethnographer focused on the rise of academic data science, she is interested in understanding and shaping emergent data science practices and norms. In particular, she focuses on efforts to harness data for societal benefit, and has both studied and helped develop the eScience Institute’s annual Data Science for Social Good program. Dr. Tanweer's dissertation, “Data science of the social: How the field is responding to ethical crisis and spreading across sectors” won the University of Washington’s 2018 Distinguished Dissertation Award in the Social Sciences. Her current roles include: eScience Institute Ethnography & Human-Centered Data Science Program Chair; Data Science Studies Special Interest Group Program Chair; Data Science for Social Good Affiliate Faculty; and the Department of Communication University of Washington.

Talk title: Data Science: Meta-Expertise or Interstitial Craft?

When: Friday 7 May, 10:00am - 11:00am AEST

Where: Zoom - join here at the time of the talk

Abstract:

As a nascent field within the academy, the contours, attributes, and bounties of data science are currently indeterminate and contested. This talk examines how participants in an initiative to establish data science at a large U.S. research university defined data science and articulated their relationships to the field. I discuss the multiple and simultaneously held ways that data science was imagined among research participants. One view, in keeping with broader popular discourses, casts data science as a form of meta-expertise that is agnostic and universally applicable to other domains of knowledge. Another view of data science, one that was far more prevalent among our research subjects, positions data science instead as emerging at the interstices of multiple domains. From this perspective, expertise in data science is a constantly moving target that requires collaboration, mobility, and an artisanal relationship with tools. I use Deleuze and Guattari’s explication of royal science versus nomad science to understand these competing views and explore the implications of this contestation for the present and future of data science in the academy.