Perspectives on society, culture and social interaction. Topics may include the self, emotions, culture, community, class, race and ethnicity, family, gender and economy. Restrictions: first-years and sophomores only. Open to juniors and seniors with permission of the course director. Enrollment limited to 25.

Perspectives on society, culture and social interaction. Topics may include the self, emotions, culture, community, class, race and ethnicity, family, gender and economy. Restrictions: first-years and sophomores only. Open to juniors and seniors with permission of the course director. Enrollment limited to 30.

Perspectives on society, culture and social interaction. Topics may include the self, emotions, culture, community, class, race and ethnicity, family, gender and economy. Restrictions: first-years and sophomores only. Open to juniors and seniors with permission of the course director. Enrollment limited to 30.

This one-semester course leverages students’ previous coursework to address a real-world data analysis problem. Students collaborate in teams on projects sponsored by academia, government or industry. Professional skills developed include: ethics, project management, collaborative software development, documentation and consulting. Regular team meetings, weekly progress reports, interim and final reports, and multiple presentations are required. Open only to Statistical and Data Science majors. Prerequisites: SDS 192, SDS 291 and CSC 111.

Offered as MTH 391 and SDS 391. Time series refers to datasets where there is a sequential order for the observations. The primary objective of time series analysis is to develop mathematical models that characterize the relationship of observed time series data. Topics in this course include perspectives from linear regression (time dependent covariates or errors), nonparametric techniques (smoothing, moving averages, nearest neighbors), and time domain models (autoregressive and moving average models, and their extensions).

Offered as MTH 320 and SDS 320. An introduction to the mathematical theory of statistics and to the application of that theory to the real world. Discussions include functions of random variables, estimation, likelihood and Bayesian methods, hypothesis testing and linear models. Prerequisites: a course in introductory statistics, MTH 212 and MTH 246, or equivalent. Enrollment limited to 20.

Students learn the social model of disability and critical disability theory as well as research design and process, and work on a research project analyzing disability inclusion public data. The statistical methods covered in this course may include logistic regression, multivariate analysis, factor analysis, etc. Students are expected to submit their final projects to a journal, conference or competition by the end of the semester. Prerequisite: SDS 201, SDS 220 or ECO 220. Restrictions: Juniors and seniors only. Enrollment limited to 12. Instructor permission required.

(Formerly MTH 291/ SDS 291). Theory and applications of regression techniques: linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.

(Formerly MTH 291/ SDS 291). Theory and applications of regression techniques: linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences. Students who have completed SDS 100 in a previous semester need not repeat it. Corequisite: SDS 100.