One of the most abundant sources of data available to social and political scientists today is text. Recent advances in Natural Language Processing (NLP) have spearheaded a text-as-data revolution, which has led social scientists to seek out new means of analyzing text data at scale. In this course, we will learn the intuition behind—and how to implement—different computational methods to process, analyze, and classify text. The course will cover Bag-of-Words (BoW) approaches, unsupervised methods, supervised and semi-supervised methods, and generative methods that use text as data, as well as how we can interpret the results obtained from applying these methods.
This course introduces students to basic statistical techniques used to estimating and testing causal relationships. Evaluating causal claims is best done using an experimental design like randomized controlled trials. However, most of the data available to political scientists is observational in nature. Drawing causal inferences from observational data is a hard task but not an impossible one, given careful treatment of the data. A series of methodological tools are available to scholars to evaluate causal arguments and hypotheses using observational data and this course introduces the most commonly used ones for cross-sectional data structures—observations of subjects (like individuals, polities, or countries) at one point or period of time. At the end of this course, students should be able to interpret many of the empirical analyses reported in political science journals and monographs and produce their own empirical analyses to estimate and test causal relationships of interest.
En este corto taller vamos a aprender las herramientas básicas para visualizar su data. La visualización de datos, ampliamente entendida, es una manera (efectiva, cuando se la hace bien) de contar una historia (la historia que tú quieres contar). Una mala visualización de datos es como una mala narrativa. Una buena visualización de datos es chefs kiss.