Over the past five years, “data science” has become a major force, as companies strive to gain insight into customer behavior, researchers look for patterns in large collections of data, and educational institutions aim to train the next generation of data scientists. (Rice has recently launched its own data science initiative.) Humanists have rich data to analyze (such as collections of texts, images, media objects, cultural information, etc) and are developing significant data-intensive research projects, but they are also raising important questions about ethics, how to handle absence and ambiguity, and the risk of reductionism.
So what are we to make of data science in the (digital) humanities, and what can humanists and cultural heritage professionals contribute? What do humanities scholars and cultural heritage professionals need to know about data science? What would a humanities data science course look like? In this Talk/Make session, I’d like to collectively sketch out a humanities data science syllabus in order to articulate key questions/themes and get started imagining a potential course. Potential models include Lauren Klein’s LMC 3206: Studies in Communication and Culture: Data and Miriam Posner’s DH 101.