Inclusive Data Literacy

(4 Credits)

In a world that is increasingly data-driven, where quantitative empirical methodologies drive leadership decisions and shape our experienced reality, basic data literacy has become as important as traditional language literacy. Equally essential for an inclusive society is a fundamental appreciation for the gaps in existing data infrastructures and potential blind spots in rigorous quantitative empirical approaches. Can evidence-based leaders avoid being "evidence-biased"? This inclusive data literacy course will present students with basic skills and foundational concepts of both rigorous program evaluation and inclusion issues. Through reading and reflection, cross-disciplinary conversation, and group project work, students gain awareness of who is represented in evidence-based policy decisions, deepen understanding of the implicit biases hidden within data structures, and practice techniques to tackle the complexities of inclusion in a data-driven future.