Data Science in the Humanities (DASH)
Any humanities student will feel at home in the data science in the humanities minor and in its research community. No previous experience with computational methods is required — the core curriculum equips students with the skills and knowledge necessary to navigate fluently the field of Digital Humanities, and those skills and that knowledge are eminently "transferable." Computational approaches to questions in the humanities are slowly migrating from the methodological fringes of the disciplines of cultural, artistic and historical inquiry; students in the minor will have the opportunity to engage with the emerging future of their fields — and to help shape that future.
A student who does have previous computational experience — a computer science major, for instance — will also benefit from the minor. Grafting that knowledge to the problems of cultural and historical analysis and working on challenging new projects within unfamiliar fields, computer-savvy students will find themselves becoming more versatile than ever (and therefore, we believe, more attractive to graduate admissions committees and potential employers).
sample courses:
While computers have changed the way we think and interact, systematic efforts to apply current technologies to the study of history and culture have been rare. This course will consider how these technologies might transform the humanities. We will explore the various ways in which ideas and data in the humanities can be represented, analyzed, and communicated using computational tools and techniques. We will also reflect on how the expansion of information technology has transformed and is continuing to transform the humanities, both with regard to their role in the university and in society at large. Readings and class work will be supplemented by small assigned digital projects culminating in a project chosen by the students themselves. No prior experience with technology is required.
A survey of statistical ideas and principles. The course will expose students to tools and techniques useful for quantitative research in the humanities, many of which will be addressed more extensively in other courses: tools for text-processing and information extraction, natural language processing techniques, clustering & classification, and graphics. The course will consider how to use qualitative data and media as input for modeling and will address the use of statistics and data visualization in academic and public discourse. By the end of the course students should be able to evaluate statistical arguments and visualizations in the humanities with appropriate appreciation and skepticism. Details. Core topics include sampling, experimentation, chance phenomena, distributions, exploration of data, measures of central tendency and variability, and methods of statistical testing and inference. In the early weeks, students will develop some facility in the use of Excel; thereafter, students will learn how to use Python or R for statistical analyses.
