This graduate course provides a foundation in the history, concepts, methodologies, and tools of digital humanities research. Students learn to critically evaluate and incorporate computational and data-driven methods into their research, and achieve a baseline fluency in accessing, filtering, and analyzing humanities datasets. No prerequisites or preexisting technical skills are required. Students working with texts, images, and artifacts are welcome. Enrollment preference will be given to students pursuing in the Graduate Certificate in Digital Humanities.
Learning Outcomes
In this class, students will:
Gain fluency in the current landscape of platforms, tools, and techniques for computational and data-driven research in the humanities.
Locate their home discipline within the wider galaxy of humanistic inquiry. How are quantitative methods and data science impacting their field, as well as the humanities writ large?
Learn about the political economy of contemporary artificial intelligence and what interventions humanities researchers offer the way these technologies construe meaning.
Prepare a research plan for a DH project, either as a conference presentation, a dissertation chapter, a standalone research article, or a curated dataset.
Read ~75-100pp / week of recent scholarship spanning history, literary studies, art history, and other humanities disciplines that are mixing data, computation, and critique.
Requirements
Attend class and participate in class discussions.
Present on one of the readings in class, by yourself or as a pair (20 min max).
Attend one DH workshop on campus (alternatively, complete one tutorial from The Programming Historian) and report back to class on the experience.