Elective Courses

The curriculum for the one-year M.A. in Digital Studies includes three elective courses of the student’s own choosing in addition to the six core courses required for the degree. The undergraduate minor allows one elective course.

M.A. students are not permitted to audit any course. Each course must be taken for credit, regardless of whether it is a required core course or an elective course. In addition, all Digital Studies courses with a “DIGS” course code must be taken for quality (letter) grades and may not be taken pass/fail.

At least one of the three elective courses for the M.A. must deal with digital computing, in some fashion, whether or not the course entails actual coding; the other two may be on any subject of interest to the student. The single elective course allowed for the undergraduate minor must pertain to digital computing.

Please note that students must obtain written approval from the Associate Director of Curriculum and Instruction of the Forum for Digital Culture to take an elective course that does not appear in the list of preapproved elective courses below. This requirement applies both to courses that deal with digital computing and courses on other topics.

Elective courses may be chosen from among the course offerings of any department or program in the Division of the Arts & Humanities or the Division of the Social Sciences of the University of Chicago, subject to the enrollment restrictions and prerequisites that may pertain to a particular course. Students will not normally be allowed to enroll in courses offered by other schools or divisions of the University (e.g., the Booth School of Business and the Harris School of Public Policy). Students who are well prepared to take such courses may be allowed to do so, in rare cases, but this must be approved by the Faculty Director.

Please note that students who have previously passed a college-level course in computer programming or statistics with a grade of B (3.0) or higher may petition the Associate Director of Curriculum and Instruction for an exemption from taking the corresponding core course, i.e., DIGS 20001/30001, “Introduction to Computer Programming Using Python,” or DIGS 20002/30002, “Introduction to Statistics Using Python.” If the petition is granted, the core course will be replaced by an additional elective course in the Autumn Quarter.

Elective Courses for Specializations in Digital Studies

There are four areas of specialization in Digital Studies for which elective courses are offered on an annual basis by the Forum for Digital Culture. Descriptions of these courses are provided below. Students in the two-year version of the M.A. program will be required to take courses in their area of specialization. Students doing a one-year M.A. who are interested in one of these areas may choose to take their elective courses in that area. However, one-year students are not required to do a specialization and may choose three unrelated electives.

Digital Texts and Culture

DIGS 20031/30031. Digital Texts I: Corpus Building and Corpus Statistics. 100 units. Winter. The purpose of this course is to introduce students in the humanities to digital methodologies for the study of texts. Students will not only learn how to construct a digital text collection but also how to process text as data. Among the various digital approaches which will be introduced in class are concordances (retrieving occurrences of words), semantic similarity detection (finding similar passages across texts), sentiment analysis, and stylometry (analysis of literary style). The course will highlight how these approaches to text can provide new avenues of research, such as tracing intellectual influence over the longue durée, or uncovering the distinguishing stylistic features of an author, work, or literary movement. Students need no prior knowledge of such methods, and the course will aim at providing both the basics of computer programming in Python and giving students the necessary tools to conduct a digital humanities project. The source material for the course will be drawn from literary sources and students will be free (and encouraged) to use texts which are relevant to their own research interests. Students will need to bring a laptop to class. Instructor: Clovis Gladstone

DIGS 20032/30032. Digital Texts II: Natural Language Processing and Deep Learning. 100 units. Spring. This course builds on DIGS 20031/30031, “Digital Texts I: Corpus Building and Corpus Statistics,” by introducing students to advanced computational methods for studying texts, including deep learning (AI), with emphasis on the needs of research in the humanities. Students will evaluate these methods and gain practical experience in applying them. Prerequisites: DIGS 20001/30001, “Introduction to Computer Programming Using Python,” DIGS 20002/30002, “Introduction to Statistics Using Python,” and DIGS 20031/30031, “Digital Texts I: Corpus Building and Corpus Statistics,” or equivalent prior preparation. Instructor: Jeffrey Tharsen

DIGS 20035/30035. Introduction to Cultural Analytics. 100 units. Spring. This course introduces students to the emerging field of cultural analytics — a field that sits at the intersection of cultural studies, information science, and the computational social sciences. At root, the field is oriented around questions of how to study the cultural past and present (whether text, image, or sound) with the aid of data-driven methods, and what such methods imply for our understanding of human culture. The course will begin with a look at how past scholars wrestled with the problem of applying numbers to cultural objects, and some of their initial attempts to do so. We then move to survey the wide variety of scholarship happening today under the influence of new digital technologies and vast new information infrastructures. How have scholars across different humanistic fields adopted new computational tools? What methodological and theoretical problems has this raised? What new discoveries has it yielded? Finally, the course will consider new research directions opened up by recent advances in artificial intelligence and the increasing convergence of cultural production with online platforms that are global in reach (e.g., TikTok, Wattpad, Netflix, Spotify). Students will engage with these questions through primary readings, attempts to replicate past studies, and by designing their own research proposals. Instructor: Hoyt Long

Digital Media and Extended Reality

DIGS 30041/MAAD 20041. Digital Media I: Game Design with Unity. 100 units. Winter. This course introduces the principles, practices, and techniques of game design. Students will develop several small games, gaining hands-on experience with the Unity development platform and the C# programming language it uses. The course takes a “ground up” approach: starting with the fundamentals of object- and component-oriented programming, then using those fundamentals to build complex, interactive experiences. While the course focuses on Unity, an introduction to software design patterns and an emphasis on a rapid feedback/iteration cycle will provide tools that translate to other game engines and creative computing projects. Through critique and the close examination of case studies from prior art, students will cultivate their critical eye and articulation, equipping them to discuss, assess, and refine games at various stages of development. Prerequisite: DIGS 20001/30001, “Introduction to Computer Programming Using Python” or an equivalent course in computer programming. Instructor: Cameron Mankin

DIGS 30042/MAAD 20042. Digital Media II: Extended Reality with Unity. 100 units. Spring. Not offered in 2024–2025. This course teaches students how to develop extended reality (XR) environments using the Unity platform, with a focus on augmented reality (AR) and some attention also to virtual reality (VR). Students regularly present their work to the instructor and fellow students, helping them refine their skills and ideas in the process of creating. By the end of the quarter, students will understand the process of transforming ideas into final products, equipping them with the necessary tools for future XR endeavors. Prerequisites: DIGS 20001/30001, “Introduction to Computer Programming Using Python” or an equivalent course in computer programming, and DIGS 30041/MAAD 20041, “Digital Media I: Game Design with Unity” or an equivalent Unity course approved by the instructor.

DIGS 30043/MAAD 20043. Digital Media III: Creating 3D Worlds with Blender. 100 units. Winter. This course introduces students to digital rendering, modeling, and sculpting using the Blender platform. Prerequisite: DIGS 20001/30001, “Introduction to Computer Programming Using Python” or an equivalent course in computer programming. Instructor: Crystal Beiersdorfer

Additional courses relevant to the specialization in Digital Media and Extended Reality are offered regularly by the Department of Cinema and Media Studies (CMST) and the Department of Computer Science (CMSC) and are listed below as preapproved electives.

Digital Art and Archaeology

DIGS 20021/30021. Digital Archaeology. 100 units. Spring. This course introduces students to a variety of computational methods used in archaeology and art history for the digital representation and analysis of cultural sites, buildings, landscapes, and artifacts. Relevant concepts and techniques are taught by means of both explanatory lectures and hands-on exercises. Software tools used in the course include ArcGIS and QGIS for geospatial data and map-creation; Agisoft Metashape for photogrammetry and 3D modeling; OCHRE for integrated multimedia data management; and Python software libraries for image analysis, feature recognition, and statistics. Gamification and the use of augmented reality and virtual reality in archaeology are discussed briefly; these topics are covered in detail in DIGS 20041/30041, “Digital Media I: Game Design with Unity,” and DIGS 20042/30042, “Digital Media II: Extended Reality with Unity.” Instructor: Andrew Wright

Two additional courses relevant to the specialization in Digital Art and Archaeology are offered annually by the Department of Middle Eastern Studies:

NEAA 20061/30061. Ancient Landscapes I. 100 units. Autumn. This is the first course in a two-course sequence that introduces students to theory and method in landscape studies and the use of Geographic Information Systems (GIS) to analyze archaeological, anthropological, historical, and environmental data. Course one covers the theoretical and methodological background necessary to understand spatial approaches to landscape and the fundamentals of using ESRI’s ArcGIS software, and further guides students in developing a research proposal. Instructor: Mehrnoush Soroush

NEAA 20062/30062. Ancient Landscapes II. 100 units. Winter. This course covers more advanced GIS-based analysis (using vector, raster, and satellite remote-sensing data) and guides students in carrying out their own spatial research project. In both courses of this two-course sequence, techniques are introduced through the discussion of case studies (focused on the archaeology of the Middle East) and through demonstration of software skills. During supervised laboratory times, the various techniques and analyses covered will be applied to sample archaeological data and also to data from a region/topic chosen by the student. Instructor: Mehrnoush Soroush

Students interested in Digital Art and Archaeology are also encouraged to take “Digital Media I: Game Design with Unity,” “Digital Media II: Extended Reality with Unity,” and “Digital Media III: Creating 3D Worlds with Blender” (described above).

Artificial Intelligence and Language

DIGS 20006/30006. Artificial Intelligence and the Humanities. 100 units. Spring. In this course we will look at artificial intelligence (AI) from the perspective of the humanities both to assess the impact of AI on the creation and study of cultural materials and to question its presuppositions. The first part of the course will survey the history of the attempts made over the years to create AI using computational methods and the philosophical critiques of those attempts. Attention will be paid both to symbolic AI that employs explicit digital representations of human knowledge and reasoning and the quite different paradigm of connectionist AI that employs neural networks and predictive models. In the latter part of the course, we will discuss the recent development of “generative AI” systems (e.g., ChatGPT) that use large “foundation models” to create remarkably human-like text and images and we will experiment with these systems via hands-on exercises. We will consider the benefits and drawbacks of such tools for research in the humanities and discuss their social and cultural impact more generally. No prerequisites. Instructor: Jeffrey Tharsen

DIGS 20009/30009. Machine Learning for the Humanities. 100 units. Autumn. Machine learning is an important aspect of artificial intelligence that uses statistical algorithms to detect patterns in digital data and make predictions on the basis of those patterns, improving the predictions iteratively over time. It is the basis of computer vision (image recognition) and machine translation of natural language (both written texts and sound recordings). So-called deep learning using many-layered neural networks is a subset of machine learning and is the basis of generative AI using large language models (e.g., ChatGPT). This course introduces machine learning with a focus on its uses in the study of human languages, culture, and history in the disciplines of the humanities. Prerequisites: DIGS 20001/30001, “Introduction to Computer Programming Using Python” and DIGS 20002/30002, “Introduction to Statistics Using Python,” or equivalent courses in computer programming and statistics. Instructor: Brooke Luetgert

Additional courses relevant to the specialization in Artificial Intelligence and Language are offered regularly by the Department of Linguistics (LING) and the Department of Philosophy (PHIL) and are listed below as preapproved electives.