The following six core courses are required for the MA and joint BA/MA in Digital Studies of Language, Culture, and History. A subset of these courses is required for the undergraduate minor and for the Graduate Certificate in Digital Studies. Technical topics not covered in these courses will be taught in a series of non-credit workshops throughout the year that students may choose to attend based on their own interests; e.g., high-performance computing (HPC), geographic information systems (GIS), the R language for data analysis, etc.
Note that the word “data” in the course titles below refers to any information that is represented digitally 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 analysis, and data publication encompass texts, images, audio, video, 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 with 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 courses. The textbook for the course is Think Python (second edition) by Allen B. Downey, which is available online, free of charge. Students who have previously passed an equivalent college-level course in computer programming with a grade of B or higher may petition the Director of Digital Studies for an exemption from taking this course and for permission to take an additional elective course instead.
DIGS 20002/30002. Data Analysis I: Introduction to Statistics. 100 units. Autumn.
This course provides an introduction to statistics and computational data analysis using Python and Jupyter Notebook. It is a prerequisite for “Data Analysis II: Data Visualization and Machine Learning” (DIGS 20004/30004) in the Winter Quarter. Topics covered include probability, distributions, and statistical inference, as well as linear regression and logistic regression. Students will gain additional practice in Python coding and will learn how to use Python libraries for statistics and plotting. The textbook for this course is OpenIntro Statistics, which is available online, free of charge. Students who have previously passed an equivalent college-level course in statistics with a grade of B or higher may petition the Director of Digital Studies for an exemption from taking this course and for permission to take an additional elective course instead.
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.
DIGS 20004/30004. Data Analysis II: Data Visualization and Machine Learning. 100 units. Winter.
This course will focus on best practices for visualizing large and complex data sets in Python. We will consider the foundations of machine learning for regression, classification, and clustering. Topics covered will include data visualization, social network analysis, principal component analysis (PCA), and the k-nearest neighbors (KNN) algorithm. The objective is to make students familiar with these methods and aware of their potential in linguistic, cultural, and historical research. Students will also expand their knowledge of applying Python in the area of data analytics and display. Prerequisites: DIGS 20001/30001, “Introduction to Computer Programming with Python” (or an equivalent course in computer programming) and DIGS 20002/30002, “Data Analysis I: Introduction to Statistics” (or an equivalent course in statistics).
DIGS 20007/30007. Introduction to Digital Humanities. 100 Units. Winter.
This course surveys the history and theory of digital computing, the ways computers have been used for research in the humanities, the philosophical issues raised by digital knowledge representation and AI, and recent theoretical debates surrounding the contested concept of “digital humanities.” Prerequisites: DIGS 20001/30001, “Introduction to Computer Programming with Python” (or an equivalent course in computer programming) and DIGS 20003/30003, “Data Management for the Humanities.” These prerequisites may be waived in some cases with the instructor’s consent.
DIGS 20005/30005. Data Publication for the Humanities. 100 units. Spring.