Data Science
Application of unsupervised machine learning methods to the linguistic, cultural, and sociological analysis of long form textual sources. Students will engage with DH theory, history, and methods, as well as large digitized text libraries, structured data (relational databases, linked data, etc.), and programming language. Knowledge of Python and familiarity with probability theory required.
T/TH 12:30-1:45
Course
Course
Exploratory text analytics concerns the application of computational and statistical methods to the interpretation of large collections of digitized written documents. The field is motivated by the research of scholars from the humanities and human sciences interested in understanding the semantic, cultural, and social dimensions of texts from historical and contemporary sources, such as novels, newspapers, and social media. The course comprises three main sections: (1) an overview of text interpretation theory combined with information theory to introduce the domain knowledge required...