We live in a financialized world dominated by financial actors, markets and institutions. From the Occupy Wall Street movement to ongoing debates about the power of big banks, finance has been seen as the culprit for the 2008 financial crisis, U.S. income and wealth inequality, and global instability. But what explains the rise of finance and how has finance gone global? How does global finance contribute to inequality within and across nations? We will tackle these questions by covering some of the recent sociological research on finance and financial globalization.

How can the social sciences benefit from remarkable advances in hardware and software that have unlocked new approaches to using text-as-data? This course interrogates the use of text-as-data from both social scientific and computational perspectives. Students will consider how meaning and context are theorized and how scale is achieved in the analysis of text by social scientists and computational experts.

From A.I.-generated art to selfies, images saturate contemporary social life. This course investigates visual imagery through the lens of sociology. In particular, it focuses on how sociologists engage with visual material in the study of society. Students will examine how sociologists use images to build and evaluate theory, create new concepts, and display their findings. Students will also explore methodological approaches sociologists draw on to incorporate images as a source of data.

In this critical survey of the main theoretical perspectives in contemporary sociology, we focus specifically on structural functionalism, symbolic interactionism, critical theory, feminism, and postmodernism. Besides gaining familiarity with these alternative perspectives, we try to identify the main axes of theoretical dispute in sociology and discuss the problems of evaluating and resolving conflict between theories.

This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.

This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.

This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.

In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.

In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.

This course includes methods for choosing, fitting, evaluating, and comparing statistical models; introduces statistical inference; and analyzes data sets taken from research projects in the natural, physical, and social sciences.