Perspectives on society, culture and social interaction. Topics may include the self, emotions, culture, community, class, race and ethnicity, family, gender and economy. Priority given to first years and sophomores. 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. Priority given to first years and sophomores. 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. Priority given to first years and sophomores. 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. Priority given to first years and sophomores. 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. Enrollment limited to 20.

Offered as PSY 364 and SDS 364. Research on intergroup relationships and an exploration of theoretical and statistical models used to study mixed interpersonal interactions. Example research projects include examining the consequences of sexual objectification for both women and men, empathetic accuracy in interracial interactions and gender inequality in household labor. A variety of skills including, but not limited to, literature review, research design, data collection, measurement evaluation, advanced data analysis and scientific writing will be developed.

Students will 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. Enrollment limited to 12. Juniors and seniors only. 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. Prerequisite: one of the following: SDS 201, PSY 201, GOV 203, SDS 220, ECO 220 or equivalent or a score of 4 or 5 on the AP Statistics examination.

(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. Prerequisite: one of the following: SDS 201, PSY 201, GOV 203, SDS 220, ECO 220 or equivalent or a score of 4 or 5 on the AP Statistics examination.

This course is not about data analysis—rather, students will learn the R programming language at a deep level. Topics may include data structures, control flow, regular expressions, functions, environments, functional programming, object-oriented programming, debugging, testing, version control, documentation, literate programming, code review and package development. The major goal for the course is to contribute to a viable, collaborative, open-source, publishable R package. Prerequisites: SDS 192 and CSC 110, or equivalent. Enrollment limited to 40.