Big Data reflects a radical shift in how we think about and investigate complex inquiries across business, education, healthcare, humanities, social science, and STEM fields. In this course, we will challenge the notion that large/hierarchical/nested datasets, called Big Data, can only be analyzed through quantitative means.

Through student-directed projects, we will explore a variety of analytical approaches using critical, feminist, intersectional, queer, indigenous, radical, and other equitable data analysis methods in collaboration with quantitative interpretations.

We will also contribute to scientific communication by editing the Wikipedia article on critical data studies (https://en.wikipedia.org/wiki/Critical_data_studies) to establish notability through updated sources, a history of this interdisciplinary subfield of data analytics, and a discussion of the contributions and controversies of critical data approaches in the scientific literature.