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, and/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 25.

Offered as GOV 338 and SDS 338. How does the behavior of a state, politician, or interest group affect the behavior of others? Does Massachusetts’s decision to legalize recreational marijuana influence Vermont’s marijuana policies? From declarations of war to the decision of who congressmembers will vote with, social scientists are increasingly looking to political networks to recognize the inter-connectedness of the world around us. This course will overview the essentials of social network analysis and how they are applied to give us a better understanding of American politics.

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. Topics 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 30. Juniors and seniors only. Instructor permission required.

This course is designed to provide an overview of fundamental topics within neuroscience & psychology and showcase the multitude of ways in which statistical and data sciences are applied in research studies to obtain results that advance our understanding. Topics will include neuroimaging, cognitive neuroscience, neurological condition, learning & memory, sleep, emotions & behavior, abnormal psychology, trauma & mental health, and research methods.

(Formerly MTH/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 the equivalent or a score of 4 or 5 on the AP Statistics examination.

(Formerly MTH/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 the equivalent or a score of 4 or 5 on the AP Statistics examination.

(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 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 111, or equivalent. Enrollment limited to 40.

This course introduces the theory and practice of data ethnography, demonstrating how qualitative data collection and analysis can be used to study of data settings and artifacts. Students will learn techniques in field-note writing, participant observation, in-depth interviewing, documentary analysis, and archival research and how they may be used to contextualize the cultural underpinnings of datasets. Students will learn how to visualize datasets in ways that foreground their sociopolitical provenance in R.