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.

The United States criminal legal system is increasingly data-driven. Technologies like recidivism algorithms and predictive policing give the appearance of numerical objectivity. But, without sufficient context, one cannot understand how these technologies work or the data that they are using. This course introduces students to the theory and practice of handling data about the United States criminal legal system. Students engage with this material through in-depth reading about the history of crime, policing, and incarceration, discussions, research design, and large data-driven projects.

(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.

(Formerly MTH/SDS 290). A survey of statistical methods needed for scientific research, including planning data collection and data analyses that provide evidence about a research hypothesis. The course can include coverage of analyses of variance, interactions, contrasts, multiple comparisons, multiple regression, factor analysis, causal inference for observational and randomized studies and graphical methods for displaying data. Special attention is given to analysis of data from student projects such as theses and special studies. Statistical software is used for data analysis.

This course covers the skills and tools needed to process, analyze and visualize data in Python and work on collaborative projects. Topics include functional and object oriented programming in Python, data wrangling in Pandas, visualization in Matplotlib in seaborn, as well as creating a reproducible workflow: debugging, testing and documenting programs, and effectively using version control. The major goal for the course is to create a viable, open-source Python package like those in the Python Package Index (PyPI). Prerequisites: SDS 192 and CSC 110. Enrollment limited to 40.

This course is not about data analysis—rather, students 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.