Why a Graduate Curriculum?

Because I’m curious, and because I think there’s something important to be learned and acted on here.

The Curriculum

The first section is a full data science curriculum, and the second is a mixed program of social science and complex systems science. The goal is to research how we parse the world and make knowledge of it, how we organize this knowledge, how we derive insights from it, how we can collectively and justly make decisions based on what we learn, and how we can build institutions together that protect and support this process. It’s a deeply interdisciplinary research program including coursework in epistemology; data modeling and knowledge representation; information management and engineering; inference and analytics; game theory and social choice theory; complex systems science; and institutional analysis and collective action. It also includes some critical theoretical work on the pitfalls and blind spots of these subjects to make sure that I don’t get too myopically, and dangerously, utopian about the ability to achieve world peace through better deliberative methods, mechanism design, and information management alone. It’s also there to make sure that I can’t pretend modeling reality is a value-neutral and apolitical act; that we are not constantly in danger of enshrining our biases in the way we choose to measure the world and shove it into categories.

Data Management and Engineering

• Modern Data Science with R by Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton
• Statistical Data Cleaning with Applications in R by Edwin de Jonge and Mark van der Loo
• Fundamentals of Data Engineering by Joe Reis and Matt Housley
• Data Management Body of Knowledge by DAMA International
• Deep R Programming by Marek Gagolewski
• The Data Warehouse Toolkit by Ralph Kimball and Margy Ross

Inference and Analytics

• Forecasting Principles and Practice 3e by Rob J. Hyndman and George Athanasopoulos
• Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
• Statistical Rethinking: A Bayesian Course with Examples in R and Stan by Richard McElreath (additional video tutorials)
• The Effect: An Introduction to Research Design and Causality by Nick Huntington-Klein (additional video tutorials)
• Text as Data: A New Framework for Machine Learning and the Social Sciences by Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart
• Rearchitecting LLMs by Pere Martra

Domain Modeling and Knowledge Representation

• An Invitation to Applied Category Theory: Seven Sketches in Compositionality by David I. Spivak
• Knowledge Representation in Bicategories of Relations by Evan Patterson
• Representing Knowledge and Querying Data using Double-Functorial Semantics by Michael Lambert and Evan Patterson

Algorithms and Society

• Fairness and Machine Learning by Solon Barocas, Moritz Hardt, and Arvind Narayanan
• Data Feminism by Catherine D’Ignazio and Lauren Klein
• The Ordinal Society by Marion Fourcade and Kieran Healy

Knowledge Creation and Power

• Seeing Like a State by James C. Scott
• Power/Knowledge by Michel Foucault
• Sorting Things Out: Classification and Its Consequences by Susan Leigh Star and Geoffrey C. Bowker

Collective Decision-Making, Institutions, and Democracy

• How Institutions Think by Mary Douglas
• Understanding Institutions: The Science and Philosophy of Living Together by Francesco Guala
• Governing the Commons by Elinor Ostrom
• Open Democracy: Reinventing Popular Rule for the Twenty-First Century by Helene Landemor

Modeling and Simulation of Complex Systems

• Growing Artificial Societies: Social Science from the Bottom Up by Joshua Epstein and Robert L. Axtell
• Agent-Based Modeling for Archaeology: Simulating the Complexity of Societies by Iza Romanowska, Colin D. Wren, and Stefani A. Crabtree
• Ex Machina: Coevolving Machines & the Origins of the Social Universe by John H. Miller
• Introduction to the Modeling and Analysis of Complex Systems by Hiroki Sayama

Computational Social Science

• Bit by Bit: Social Research in the Digital Age by Matthew Salganik
• Networks, Crowds, and Markets by David Easley and Jon Kleinberg
• A Course in Game Theory by Martin J Osborne and Ariel Rubinstein
• Handbook of Computational Social Choice edited by Felix Brandt, Vincent Conitzer, Ulle Endriss, Jerome Lang, and Ariel D. Procaccia

Epistemology and Philosophy of Science

• Philosophy of Science: A Contemporary Introduction by Alex Rosenberg and Lee McIntyre
• Philosophy of Science: Contemporary Readings edited by Yuri Balashov and Alex Rosenberg
• Epistemology: A Contemporary Introduction by Robert Audi
• Epistemology: Contemporary Readings edited by Michael Huemer

Philosophy of Social Science

• The Logic of Social Science by James Mahoney
• Orienting to Chance: Probabilism and the Future of Social Theory Michael Strand and Omar Lizardo
• Model Cases: On Canonical Research Objects and Sites by Monika Krause