This course explores big data as both technical practice and a psychological phenomenon. We examine how data analysts, modelers, and designers rely on cognitive processes such as perception, categorization, heuristics, attribution, and memory. Students evaluate how these psychological mechanisms become encoded in models and algorithms, influencing classification, prediction, and decision-making across social domains. We explore how big data functions as an epistemology—shaping what counts as knowledge, evidence, and truth—and how algorithmic systems construct representations of people and communities. We also consider how individuals and groups respond to data-driven systems through trust, resistance, adaptation, and contestation. Finally, we assess the limits of quantification, and the role qualitative, contextual, and community-based methods play in offering alternative forms of knowledge.

Through interdisciplinary readings and critical reflection, students develop a sophisticated understanding of the interplay between human psychology and data-intensive technologies. This course is ideal for computer science students who want to learn more about psychological processes at work in data technologies, and psychology students who want to bring psychological expertise to data technologies and processes.