Computational Methods for Social Sciences Using Large Data

Professor Keita Omi

About Course: In this course, students will analyze social and political science data and replicate results found in the academic literature. To be more specific, students will learn the basics of programming in R, and learn to apply computational analysis techniques on large datasets. In addition, students will be equipped with fundamental concepts and data science skills for developing their own research.

Empirical studies in social and political sciences are entering a new age of computational analysis where massive data, both digital and analog, can be analyzed systematically and comprehensively. Also, scientific visualization enables us to identify patterns, values on some of the variables identified as important, and relationships among variables we would otherwise overlook. Further, it helps us think both creatively and critically about precise assumptions underlying a limited set of parameters and variables.

Examples include digital ethnography, text analysis of political documents with machine learning, and social network analyses of political groups and individuals. How can we take advantage of these new data sources and improve our understanding of social and political phenomena that we encounter and observe in our daily life and even in the global arena? This course introduces to students a method of computational data analysis and its applications in social and political sciences research manners. This course is very much a hands-on class in that it puts the students in the role of social and political science researchers. In this course, the importance of social and political context and application in learning is emphasized, which situates learners in directionally aligned with socially constructing and experiential and complex learning by doing. Approximately, half of our sessions will be devoted to computer lab sessions involving hands-on data handling and analysis; and the other half will be devoted to lectures and pro-seminar sessions. This class will be being delivered in a flipped classroom format. Students will be responsible for working through the materials provided in advance of the scheduled class time and topics and challenges discussed in class at their own pace.