HIST 7219: Humanities Data Analysis | Thursday 4:30-7:00 | Holmes Hall 400B
Weekly Coding Work Session: T 9:30(ish)-11am
Professor: Ryan Cordell | Office: Nightingale Hall 415 | Office Hours: M 10-11am, R 2:30-4:00pm, or by appointment
TA: Jonathan Fitzgerald | Office: Nightingale Hall 401 | Office Hours: T 10am-12pm
Humanities scholars in the 21st century grapple with ever larger and more diverse kinds of evidence created through the massive digitization of historical, literary, and cultural heritage materials. The creation, representation, analysis, and visualization of humanistic data constitute engagingly thorny challenges which this seminar will explore from both practical and critical perspectives.
Our readings, discussions, and classroom labs will ask questions such as:
- What kinds of data exist for humanistic research? Where can researchers find such data and how can they be used? How do the priorities of researchers and cultural heritage institutions (museums, libraries, universities) align, and where do they diverge?
- What light can algorithmic or computational approaches such as text analysis or topic modeling shed on pressing questions in humanistic scholarship? How can the humanities adapt methods from more quantitative disciplines, and where does such methodological appropriation falter?
- How might different modes of visualization, such as graphs, maps, or networks, contribute to answering humanistic questions? What makes visualization effective or ineffective? Where might data analysis intersect with issues in art and design?
Our most ambitious goal in this class will be to explore the new emerging forms of data analysis taking place in humanities scholarship, both by learning to apply algorithms and learning to better investigate the presuppositions and biases of digital objects. We will aim to emerge more sophisticated in our use of computational techniques and much more informed about how other scholars use them. Students will develop final projects using a data set pertinent to their own research or areas of interest.
Humanities Data Analysis requires no previous coursework, though having taken an introduction to digital humanities would be useful. We will focus on developing students’ skills working with tabular and textual data, largely using the programming language R. Our readings will be similarly focused, so students should not expect an overview of the wider horizon of topics and methodologies that fall under the heading “DH.” Students need not have any prior experience with R for the course, which will begin at the beginning, but students should be willing to experiment with new tools and learn new technical skills throughout the semester. The course will be structured as a workshop—we’ll be working on some problems together without a predetermined answer. I hope that will be cause for excitement rather than trepidation, but Caveat studiosum.
Research or Independent Study Option
Students who wish to take this course as a research class or independent study may do so. Exactly what these mean may vary by department. But the key thing distinguishing the research option from the normal class is that you will be expected to apply the methodologies of the course to your own sources of historical or literary data, textual or tabular. Be aware this may mean a significant outlay, particularly in the early weeks, of cleaning and tidying data in ways we will touch on only tangentially in the course itself. You may, in other words, be required to lay a bit of your own foundation, but I will be happy to advise as you do so.