Sociology of Food

Using theoretical frameworks from environmental sociology, political and economic sociology, and sociology of culture, this course examines how social structures shape the way food is produced, prepared and consumed. This course investigates political and environmental dynamics that structure food systems and practices and considers inequalities related to food at the local and global levels. Finally, students explore food movements and investigate ideas for creating more equitable and sustainable practices. Prerequisite: SOC 101. Enrollment limited to 35.

Qualitative Methods

Qualitative research methods offer a means of gaining insight and understanding into complex perspectives held by people about social practices and social phenomena. Whereas good quantitative research captures scale, good qualitative research reaches the depth of perceptions, views, experiences, behaviors and beliefs. Qualitative research deals with meanings; it seeks to understand not just what people do, but why they choose to do what they do.

Introduction to Sociology

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.

Introduction to Sociology

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.

Introduction to Sociology

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.

Capstone in SDS

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.

Mathematical Statistics

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.

Sem:T-Coral Reef

Students develop the skills and tools needed to process, analyze and visualize data related to large-scale coral reef conservation and management in R. Specifically, students work to collate data from NGOs, governments and academic researchers to assess changes in coral cover and community structure across the Caribbean. Quantifying these changes across spatial scales within the basin is essential in planning and managing the coral reefs of today and those of the future.

Modeling for Machine Learning

In the era of “big data,” statistical models are becoming increasingly sophisticated. This course begins with linear regression models and introduces students to a variety of techniques for learning from data, as well as principled methods for assessing and comparing models. Topics include bias-variance trade-off, resampling and cross-validation, linear model selection and regularization, classification and regression trees, bagging, boosting, random forests, support vector machines, generalized additive models, principal component analysis, unsupervised learning and k-means clustering.

Multiple Regression

(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.
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