This project analyzes how data scientists approach, organize, analyze, and visualize the world with and through data structures, computational algorithms, and statistical techniques. We are particularly interested in the oft-invisible and under-articulated forms of human work constituting data science learning, research, and practice. We are asking questions like the following. What forms of human and technical work constitute data science? How do data scientists situate and evaluate data science results to make them meaningful in computational, business, and social contexts? Using ethnographic research methods, this project explores such questions in two empirical settings: academic data science learning learning environments and corporate data science teams.
Participants: Samir Passi, Phoebe Sengers, Steven Jackson, David Mimno