Digital Humanities

Grad course, taught in Spring 2013-2014 at UIUC, iSchool by Jana Diesner

We meet Friday 1pm to 3.50 pm

Short description: How can computational methods and technologies be used to answer substantive about the humanities and to advance these disciplines? What have people learned about their domains by doing so? We address these questions in 590DH.
This interdisciplinary course introduces students to fundamental concepts, methods and applications in the digital humanities (DH). Students gain expertise and acquire hands-on methodological skills that empower them to become literate in DH and to engage in DH research projects. The lectures are designed to introduce and discuss major concepts and techniques. We examine studies of computational tools and methods applied to humanities material in the context of research, and consider the implications of such projects for libraries. The lab sessions and assignments provide training in putting these techniques into action. Guest speakers bring the knowledge and skills into application settings. At the end of the course, students will be able to participate in DH research and to critically assess DH studies.

Selection of themes covered in lectures and guest lectures:

• DH and electronic publishing, markup schemas
  • includes guest lecture by Dan Stacy, GSLIS Visiting Librarian & Visiting Assistant Professor of Library Administration
• DH and librarianship, data curation
  • includes guest lecture by Harriett Green, English and Digital Humanities Librarian & Assistant Professor of Library Administration, UIUC
• DH and History
  • Guest lecture by Susie Pak, Assistant Professor, Department of History, St. John's University
• DH and (high performance) computing
  • includes guest lecture by Michael Simeone, Assoc. Director for Research and Interdisciplinary Studies, Institute for Computing in Humanities, Arts, and Social Science (I-CHASS)

Skills taught in labs and applied to humanities material:

• Text Mining
  • Identification of key terms and themes
  • Classification
  • Summarization (Topic Modeling)
  • Sentiment Analysis
  • Converting texts into networks
• Network Analysis
  • Network data collection
  • Network analysis/ network metrics
  • Network Visualization
• Computational Modeling and Simulation
  • Agent-based simulations of problems from political science, anthropology and others fields
• Data Visualization
  • Computational visual analytics in Processing
• Data Mining
  • Basic computational statistical analysis in R
    

No prerequisites, no programming skills required.  
Credit: 4