How can scholars use computational and digital methods to advance their research without sacrificing the interpretive, nuanced, and contextually situated stance of the humanities and humanistic social sciences?  The DH Practicum course challenges students to think of their humanities research materials as data without reducing them to data.  In this course, the digital humanities are framed as a set of various intellectual pursuits rather than a unified field; each student is encouraged to work within the norms of their particular discipline, using technical methods to generate insights that advance their individual humanistic goals. 

Our class will typically draws students from at least six or seven departments, reflecting the expansive multidisciplinary appeal of the digital humanities; course objectives must thus reflect students' individual scholarly goals and their departments' expectations. One student may build an online database and archive; another may explore manuscript digitization enabled by AI-powered optical character recognition; while a third  may pursue large-corpus text analytics using advanced computational tools.  In addition to identifying and justifying technical methods that advance their scholarly goals, students will gain the skills to manage humanities data, develop technical workflows, and transfer technical skills to others.  

We'll organize our twice-weekly class schedule by alternating between theory and practice: we will have lectures and discuss readings in seminar format on Mondays, then undertake into our technological explorations in studio sessions on Wednesdays. Within the first two weeks of class, student are expected to assemble a set of research materials which may be explored using digital and/or computational approaches. 

Readings will be drawn from general DH studies and from specific disciplinary essays on the uses of technology for research.  We will also engage with information theory, science and technology studies, and selected recent writings on the theory and practice of AI. The reading load is deliberately moderate, because students are expected to devote significant time to exploring and practicing new technical skills.

Assignments:

1. Description of Research Materials
2. Data Management Plan
3. Survey of Technologies for Research
4. Technical Learning Plan
5. Annotated DH Theory Bibliography
6. Workflow Analysis and Graphic (group project)
7. Technical Workshop Slides
8. Technical Learning Retrospective