Abstract
Abstract: Many scientific fields now benefit from ‘Big Data.’ Yet along with large datasets come an abundance of computational and statistical techniques to analyze them. Many of these techniques have not been subject to sustained philosophical scrutiny. This is in part because the scant literature on philosophy of data science often focuses on hypothesis confirmation as the primary end of data analysis. Yet there are many scientific contexts in which generation—of hypotheses, of categories, of methods—is at least as important an aim. This symposium will contribute to debates about realism, natural kinds, exploratory data analysis, and the value-ladenness of science through the lens of philosophy of data science, opening critical discussion about the nature of data and the emerging methods and practices used to foster scientific knowledge.
