Economy & Society

Undergraduate (Upper Division)

In this remote course, students learn how sociologists study and think about economic processes as the products of social action and social structures, from the networks that bond and shape market transactions, to the states and organizations that set the guidelines for trading, commerce, and the development of capital. In focusing our conversations, Economy & Society studies the growth of economic inequality as a particularly important and pressing issue. In understanding how economic inequality is produced by social action and structures we investigate how economic outcomes are shaped by class, gender, and race. For this, we read about how meritocracy, innovation, professions, careers, organizations, credit, and prices ultimately encode the weight of class, race, and gender in the economy. 

Syllabus available here

Data & Society

undergraduate (upper division)

This class introduces students to current debates about data, algorithms, information technology, and their relevance to global societies. The course requires students to read, participate, discuss and engage with current debates about ‘data’, ‘algorithms’, and ‘artificial intelligence’ as both extensions of past practices and biases and new, still congealing socio-technical domains. At its core, this course invites students to think about how algorithms, data, artificial intelligence, and so forth transform (or not) the world of work, our relation to others, our institutions of governance and employment, our sense of self, and what we can see and say about the world.

Syllabus available here

Science, Technology & Society

undergraduate (Lower division)

This class offers an introduction to the history, sociology, and anthropology of science and technology by exploring the position, responsibility, and challenges of scientific knowledge in contemporary societies. The class aims to provide students with a critical yet constructive perspective on science and technology. For this, the course challenges conventional wisdom about scientific inquiry, discusses tensions at the core of sociotechnical expertise, explores the long histories behind our societies’ engagement with nature and the environment, and reflects on the possible futures that might spring from today’s scientific endeavors. Traditional sociological approaches to science and technology are critical and this course is no exception: I expect students to develop a better appreciation of the limits and problematic legacies of scientific practice and discourse, as well as of the complex and unequal politics of technology and innovation. But while critical, the course is also a space for defending the role of science and technology in an age of great uncertainty and as key to our collective survival. The class invites students to think about the reinvention of science as a challenge that transects personal and institutional responsibilities around how knowledge is constituted, disseminated, utilized, and valued. 


Big Data for Social Scientists


This class introduces students to critical perspectives on data, data science methods, machine learning, and algorithms from and for the social sciences. This class works as a workshop: students work in groups on original research projects spanning three thematic units (plus additional on-demand topics) that cover core competencies of computational social scientists.


Quantitative Methods I


This class introduces students to basic concepts in quantitative social science, workflow and including data cleaning best practices, statistical inference, hypothesis testing, and generalized linear regressions.   

Quantitative Methods II


This class offers a survey of advanced topics in quantitative methods for the social sciences, including a more detailed study of generalized linear models and their expansions, causal inference, and ‘difficult data’