Core Courses in Digital Studies
he following six core courses are required for the Master of Arts in Digital Studies of Language, Culture, and History and the joint BA/MA program. A subset of these courses is required for the undergraduate minor and for the Graduate Certificate in Digital Studies.
Digital Studies students in the MA, BA/MA, undergraduate minor, and Graduate Certificate programs are guaranteed a place in the DIGS core courses listed below. Students in other programs may be permitted to enroll if there are any remaining spaces. For this reason, enrollment in DIGS courses requires the consent of the instructor.
The core courses cover the basics of computer programming and statistics using the Python programming language; the history and theory of computing in general and of computing for humanities in particular; and the management, visualization, and publication of data in the humanities. Technical topics not covered in these courses are taught via non-credit tutorials.
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.
Course Descriptions
Note that the word “data” in the course titles below refers to any information represented in a computer in the form of binary digits or “bits” (ones and zeroes) and is not restricted to quantitative information. The courses on data management, data visualization, and data publication encompass texts, images, audio and video recordings, geospatial mapping data, 3D models, social media, video games, computer-generated imagery, virtual reality, and every other kind of digital data. Even scholarly articles and monographs, when represented digitally, constitute “data” that can be analyzed computationally to extract information and construct new forms of information.
DIGS 20001/30001. Introduction to Computer Programming Using Python. 100 units. Autumn.
This course provides an introduction to computer programming and computational concepts using the Python programming language. Students are also introduced to the use of Visual Studio Code as an industry-standard source code editor. This course is a prerequisite for most of the other Digital Studies (DIGS) courses. Students enrolled in one of the Digital Studies programs (MA, joint BA/MA, undergraduate minor, or graduate certificate) who have previously passed an equivalent college-level course in computer programming with a grade of B (3.0) or higher may petition the Associate Director of Curriculum and Instruction of the Forum for Digital Culture for an exemption from taking this course and permission to take an additional elective course instead. Instructor: Brooke Luetgert
DIGS 20002/30002. Introduction to Statistics Using Python. 100 units. Winter.
This course provides an introduction to statistics and computational data analysis using Python and Jupyter Notebook. It is a prerequisite for “Data Visualization for the Humanities” (DIGS 20004/30004) in the Winter Quarter. Topics covered include: probability, distributions, and statistical inference; linear regression and logistic regression; principal component analysis; and a brief overview of machine learning. Students will gain additional practice in Python coding and will learn how to use Python libraries for statistics. Students enrolled in one of the Digital Studies programs (MA, joint BA/MA, undergraduate minor, or graduate certificate) who have previously passed an equivalent college-level course in statistics with a grade of B (3.0) or higher may petition the Associate Director of Curriculum and Instruction of the Forum for Digital Culture for an exemption from taking this course and permission to take an additional elective course instead. Instructor: Brooke Luetgert
DIGS 20003/30003. Data Management for the Humanities. 100 units. Autumn.
This course introduces concepts and techniques related to the representation and management of digital data with emphasis on the forms of data encountered in the humanities. Topics covered include: (1) digital text encoding using the Unicode and XML standards, with attention to the TEI-XML tagging scheme of the Text Encoding Initiative; (2) digital typefaces (“fonts”) for displaying encoded characters; (3) digital encoding of 2D images, 3D models, sound, and video; (4) database models and querying languages (especially SQL for relational databases and SPARQL for non-relational RDF-graph databases), with attention to methods for integrating and querying the kinds of semi-structured and heterogeneous data characteristic of the humanities; (5) ontologies, the Semantic Web, and related technical standards; and (6) cartographic concepts (e.g., coordinate systems and map projections) and the basics of geospatial data management using Geographic Information Systems. This course has no prerequisite; i.e., prior knowledge of computer programming is not required. Instructor: Miller Prosser
DIGS 20004/30004. Data Visualization for the Humanities. 100 units. Spring.
This course introduces best practices for visualizing data sets to assist the study of human languages, cultures, and history. Python-based tools will be used to create data visualizations, enhancing familiarity with that programming language. The emphasis will be on displaying data in a clear and elegant manner that is appropriate for the type of information we wish to showcase. Students will learn exploratory and explanatory data visualization techniques both to analyze the data and to communicate ideas via data-based storytelling. Possible biases in the way data is presented will be noted so students can learn to guard against them. Examples of data sets derived from research in the humanities will be used to develop creative and technical skills to produce standard 2D and 3D plots for descriptive summaries of the data, to identify outliers, and to do statistical correlations and model predictions that reveal underlying trends and potential anomalies. Social network analysis (a method widely used in the humanities and social sciences) will also be introduced, as well as tools for data visualization using geographical maps and timelines. Prerequisites: DIGS 20001/30001, “Introduction to Computer Programming Using Python,” or an equivalent course in computer programming and DIGS 20002/30002, “Introduction to Statistics Using Python,” or an equivalent course in statistics. Instructor: Brooke Luetgert
DIGS 20005/30005. Data Publication for the Humanities. 100 units. Winter.
This course introduces software techniques and tools for building Web browser apps written in HTML5, CSS, and JavaScript with emphasis on user interfaces for presenting information to researchers and students in the humanities. Students will take an active role in evaluating approaches and outcomes of existing digital publications. Topics covered include: (1) the use of application programming interfaces (APIs) to integrate into Web apps the various analysis, visualization, and database services provided by external systems; (2) the transformation of data into formats appropriate for publication on the Web; and (3) the nature of data in the humanities as it pertains to digital publication. Prerequisite: DIGS 20001/30001, “Introduction to Computer Programming Using Python” or an equivalent course in computer programming. Instructors: Firat Ciftci
DIGS 20007/30007. History and Theory of Computing for the Humanities. 100 Units. Autumn.
This course surveys: (1) the history and theory of digital computing, including the basics of computer architecture; (2) how computers have been used in the humanities; (3) debates about the contested concept “digital humanities”; and (4) the philosophical issues raised by digital knowledge representation and artificial intelligence. Instructor: David Schloen