Complexity Explorer Santa Few Institute

The Foundations & Applications of Humanities Analytics course is aimed at a broad range of humanities scholars. The course aims to empower scholars in the humanities by eliminating the “black box” of computational text analysis. Participants will gain a theoretical and practical understanding of text analysis methods, and will learn how to extract content and derive meaning from digital sources, enabling new humanities scholarship.

The online course will interweave lectures and assignments that impart a foundational and philosophical understanding of humanities analytics techniques with those that apply this foundational understanding to real-world data sets arising in literary, cultural, and political contexts. Supplementary resources will introduce the necessary steps to get started with Python programming and Jupyter notebooks, though programming skills will not be taught – or required – during the course.

The project Foundations and Applications of Humanities Analytics has been made possible in part by a major grant from the National Endowment for the Humanities: Exploring the human endeavor, under Federal Award ID Number HT-272418-20. Any views, findings, conclusions, or recommendations expressed in this course do not necessarily represent those of the National Endowment for the Humanities.

LEAD INSTRUCTORS

David Kinney studies epistemology, in particular, the selective acquisition of knowledge. His research applies the principles of probability theory to understand the causal structure of systems in scientific contexts, the formation of group beliefs, and the foundations of scientific reasoning. He has taught logic, philosophy of science, and political philosophy, among other subjects. In the Foundations & Applications of Humanities Analytics course, he will leverage his expertise in probability and information theory, helping students without a mathematics background achieve conceptual mastery.

Simon DeDeo leads the Laboratory for Social Minds at Carnegie Mellon University’s Department of Social and Decision Science, where his research group makes use of text sources—from French Revolutionary records and Enlightenment-era scientific communication to online conspiracy theorists and Harry Potter fan fiction—to define factors that influence how and when novel ideas emerge and become accepted. He has taught courses on cognitive and social science, large-scale social phenomena, and research methods in informatics and computing. He leads the major data science research practicum for students in the humanities, psychology, and economics at CMU. Within the FAHA project, he will provide expertise in the application of information-theoretic and machine learning techniques to case studies in literature and history.

GUEST LECTURERS

Nan Z. Da	Lauren Klein	Julia Lefkowitz
Marco Buongiorno Nardelli	Richard Jean So

TEACHING ASSISTANT

Steph Buongiorno is a doctoral candidate in the Applied Science department at Southern Methodist University’s Lyle School of Engineering. She builds data sets and computational tools for data analytics in the humanities and across the information and social sciences. Steph has applied to her skills to a diverse set of problems including human trafficking, global urbanization and housing affordability, and political discourse.