Core Courses

Python code

The following 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.

DIGS 20001/30001. Introduction to Computer Programming. 100 units. Autumn.
This course provides an introduction to computer programming and computational concepts using the Python programming language. It is a prerequisite for many 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 taken an equivalent course in computer programming may request an exemption from taking this DIGS course, subject to the approval of the Director of Digital Studies.

DIGS 20002/30002. Data Analysis for the Humanities I. 100 units. Autumn.
This course provides an introduction to statistics and computational data analysis. It is a prerequisite for Data Analysis for the Humanities II and III (DIGS 20004/30004 and 20006/30006). Topics covered include probability, distributions, and statistical inference, as well as linear regression and logistic regression. Students will learn how to use Python libraries for statistics and plotting within Jupyter Notebooks. The textbook for this course is OpenIntro Statistics, which is available online, free of charge. Students who have taken the University of Chicago course STAT 22000 or an equivalent course in statistics may request an exemption from taking this DIGS course, subject to the approval of the Director of Digital Studies.

DIGS 20003/30003. Data Management for the Humanities. 100 units. Winter.
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 for the Humanities II. 100 units. Winter.
This course builds on DIGS 20002/30002, “Data Analysis for the Humanities I,” by introducing students to the R language and R packages for data analysis. Topics covered include data visualization, principal component analysis, social network analysis, geospatial data analysis, and high-performance computing techniques for analyzing large datasets. The goal is to make students familiar with these methods and aware of their role in linguistic, cultural, and historical studies, as a basis for further study of these methods. Prerequisites: DIGS 20001/30001, Introduction to Computer Programming (or an equivalent course in computer programming) and DIGS 20002/30002, Data Analysis for the Humanities I,” or an equivalent course in statistics.

DIGS 20005/30005. Data Publication for the Humanities. 100 units. Spring.
This course introduces software techniques and tools for building Web browser apps written in HTML5, CSS, and JavaScript with emphasis on presenting information to researchers and students in the humanities. 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 use of persistent identifiers for reliable citation of published data and the problems of archiving and preserving scholarly data. Prerequisite: DIGS 20001/30001, Introduction to Computer Programming, or an equivalent course in computer programming.

DIGS 20006/30006. Data Analysis for the Humanities III. 100 units. Spring.
This course focuses on applications in the humanities of deep neural networks and machine learning, which constitute one form of artificial intelligence (AI). Topics covered include natural language processing and machine translation, audio analysis (e.g., speech recognition and musical analysis), image analysis (computer vision), and the philosophical issues raised by artificial intelligence, with a focus on non-symbolic (second-wave) AI based on deep learning. Prerequisites: DIGS 20001/30001, Introduction to Computer Programming (or an equivalent course in computer programming), and DIGS 20002/30002, Data Analysis for the Humanities I” (or an equivalent course in statistics).

DIGS 20007/30007. Introduction to Digital Humanities. 100 Units. Autumn.
This course surveys the history and theory of digital computing, the various uses of computers in the humanities, and recent debates in digital humanities. This course has no prerequisite; i.e., prior knowledge of computer programming is not required.

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