Three timely courses for graduate students interested in digital humanities and computational approaches to social science.
ILS 695 Computational Text Analysis
Computational analysis of textual data has become increasingly important in the worlds of digital humanities, digital history, data science, and computational social science. Researchers can now use computers to “read” thousands of novels at a time, to navigate and map complicated sets of digitized archives or newspapers, to categorize and analyze massive datasets of tweets or blog posts, or to build concordances and other reference materials — often with surprising ease. But what are we really doing when we use computational text analysis in our research? This course will provide an introduction to both the theory and practice of this new field. Instructors: Dr. Matthew Hannah, assistant professor of digital humanities, and Trevor Burrows, postdoctoral researcher, Purdue University Libraries and School of Information Studies
ILS 595 Data Management & Curation for Qualitative Research
This course offers an interdisciplinary introduction to data management and curation for qualitative research, with a focus on the use, value and organization of data, materials, infrastructure, tools and scholarly communication. This course is aimed at both qualitative researchers and those interested in the curation and stewardship of qualitative research data and materials within library, museum, archival and gallery settings. The course will both introduce literature concerning ethical and legal considerations of data management and curation, and provide the opportunity for hands-on data and digital literacy skills development. The course will culminate in a semester digital/data curation project.
ILS 230 Data Science and Society
This course provides an introduction to Ethical, Legal, Social Issues (ELSI) in Data Science. Students will be introduced to interdisciplinary theoretical and practical frameworks that can aid in exploring the impact and role of Data Science in society. This is a writing intensive course. Students will work individually and on collaborative assignments. Course Learning Objectives: • Engage in current debates surrounding professional and research ethics, roles and responsibilities in Data Science.
• Examine emerging legal and policy issues which impact Data Science.
• Critically reflect on the relationship between Data Science and political, social and cultural change.
• Learn collaboration, public engagement and scholarly communication skills.