Learn quantitative and statistical methods, programming and computational skills, quantitative integration and research design, math and data communication.
Three courses in applied statistics
Two classes in computation
One course in social science research design
Calculus and Math Tools for the Social Sciences
Two additional quantitative electives
Total Course Requirements: 9 courses. Up to 3 courses can double count toward a student’s other joint AOC.
Language: R and Python must be utilized as the dominant language in at least one course each.
Distribution: Primarily quantitative coursework must be undertaken in at least two different social science fields: Psychology, Economics, Political Science, Sociology, Geography, Anthropology, and History. Primarily substantive courses in those fields cannot count for this distribution requirement.
Practice: The student must complete a quantitatively oriented independent research project. Typically this is met through one of the following paths:
Identical to joint AOC, but with only one elective and without the second computation course. Up to 2 courses can double count toward student’s primary AOC. Total course requirements: 7 courses.
Quantitative & Statistical Methods: Students will have an understanding of quantitative analyses conducted in social science research and be able to conduct data analysis using descriptive, inferential, and visual statistical methods using appropriate tools.
Programming & Computational Skills: Students will learn basic programming skills and be able to utilize appropriate tools for programming tasks, data management, and statistical methods.
Quantitative Integration & Research Design: Students will demonstrate the ability to understand social science research methodology and integrate quantitative analysis with substantive social science theory and data across multiple fields.
Mathematics: Students will demonstrate the ability to understand the mathematical underpinnings of statistical models.
Data Communication & Practice: Students will learn how to describe the implications of their research to a non-expert audience and will demonstrate strong written and oral communication skills when presenting the results of their research.