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Course Content
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Empirical studies in political science is entering a new era of “Big Data” where a diverse range of data sources have become available to researchers. Examples include network data from political campaigns, data from social media generated by individuals, campaign contribution and lobbying expenditure made by firms and individuals, and massive amount of international trade flows data. How can we take advantage of these new data sources and improve our understanding of politics? This course introduces various tools at our disposal, but what questions about politics are we interested in and able to answer? We will begin answering this question by reviewing basic probability and statistics using examples from politics. We will then turn to an in-depth discussion of the basics of causal inference and the limitations of experimental and observational methods in the study of social phenomena, culminating in tutorials on the use of matching and linear regression for causal inference. The course will then shift to a set of cutting edge methods related to the “big data” revolution, including language processing, network analysis, machine learning and data visualization techniques.
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