This talk introduces Hogan’s book From Social Science to Data Science. Superficially, the book describes key skills for manipulating data in Python. Yet the book departs from a computer science textbook and towards social data science as a paradigm in its own right. It does this by framing coding as a reflexive practice for considering how we measure and structure the world, learning who gets to measure, and assessing what scales work for what kind of measurement. Such a framing opens a space for both qualitative and quantitative work in SDS given its newfound epistemelogical rather than methodological focus.

At the same time, such a focus helps us appreciate (social) data science’s distinctiveness from econometrics, statistics, computer science, and analytic or mathematical sociology. Thus, the talk concludes by reflecting on the potential for social data science to be considered an academic discipline rather than mere practice.