CAS PO 399 Data Science for Politics
Data science is changing how we understand and study politics, policy, and decision-making. This course introduces students to the fundamental tools of data science, including collecting, modeling, and visualizing data, and how to apply these tools to study political and policy questions. Effective Spring 2020, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning I, Digital/Multimedia Expression.
CAS PO 501 Formal Political Theory
Undergraduate Prerequisites: PO 111, 141, 151, or 171. Calculus (MA 121, 123, or 127) and probability (MA 113, 115, or 213) are helpful, but not required.
Graduate Prerequisites: completion of BU Social Science Math Boot Camp. Some additional familiarity with calculus and microeconomics is helpful, but not required.
A course on formal theory, covering decision theory, game theory, and social choice theory. Topics include spatial models, electoral competition, bargaining, deterrence, and signaling models. Effective Fall 2019, this course fulfills a single unit in each of the following BU Hub areas: Social Inquiry II, Quantitative Reasoning II, Critical Thinking.
CAS PO 502 Political Analysis: A Primer
An introduction to the research methods used to make claims about political phenomena. Addresses both qualitative and quantitative approaches, and focuses on applied empirical political science, including data description, research design, significance testing, surveys and experiments. Required for PO Honors Program.
GRS PO 840 Political Analysis
An introduction to the methodology of social science as applied to the study of politics. Includes discussion of core debates in philosophy of science, various approaches to political science, and questions of research design.
GRS PO 841 Quantitative Research
An applied graduate level introduction to probability, descriptive statistics, hypothesis testing, and ordinary least squares regression analysis.
GRS PO 843 Advanced Techniques in Political Analysis
Applied course in quantitative empirical analysis using maximum-likelihood estimation for data that are discrete, truncated, or non-normally distributed. Topics include hypothesis testing in binary, multinomial, count and ordered response models.