(Offered as HIST 258 and SWAG 258) This course will examine the history of medicine in the U.S. with a focus on the roots and persistence of structural violence, discrimination, and stigma. The history of medicine was long viewed as the study of the development of new approaches to disease prevention and treatment. However, pathbreaking scholarship on the racist roots of American medicine has called for an examination of how broader social, cultural, and political norms and values shaped medical training and practices. Slavery and colonialism transformed early modern medicine.

(Offered as ANTH 209, SOCI 207, and SWAG 209) This seminar uses feminist theory and methods to consider scientific practice and the production of medicoscientific knowledge. We will explore how medicine and science reflects and reinforces social relations, positions, and hierarchies as well as whether and how medicoscientific practice and knowledge might be made more accurate and socially beneficial.

Normally to be taken as a single course but, with permission of the thesis advisor, as a double course (499D) as well.

How to handle overenrollment: null

Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: N/A

Spring semester. The Department.

How to handle overenrollment: null

Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: quantitative work, writing intensive, use of computational software, independent research

(Offered as STAT 370 and MATH 370) This course examines the theory underlying common statistical procedures including visualization, exploratory analysis, estimation, hypothesis testing, modeling, and Bayesian inference. Topics include maximum likelihood estimators, sufficient statistics, confidence intervals, hypothesis testing and test selection, non-parametric procedures, and linear models.

Requisite:

Survival analysis is a flexible and powerful approach to model data where time to an event is of interest. Survival data arise in many settings where we need to act before all events are observed (hence some are censored or truncated).  Students will develop an understanding of the theory and applications of survival analysis, including parametric, semi-parametric, and non-parametric survival models using software to carry out analyses and simulation. The class will provide opportunities to deepen understanding through analysis of case studies and published papers.

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications.

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications.

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111, STAT 135, or STAT 136. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment.

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111, STAT 135, or STAT 136. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment.