(Offered as CLAS 123 and SWAG 123) We read in English the major authors from Homer in the 8th century BCE to Plato in the 4th century in order to trace the emergence of epic, lyric poetry, tragedy, comedy, history, and philosophy. How did the Greek enlightenment, and through it Western culture, emerge from a few generations of people moving around a rocky archipelago? How did folklore and myth develop into various forms of “rationality”: science, history, and philosophy? What are the implications of male control over public and private life and the written record?

(Offered as ANTH 211 and SWAG 108) This course introduces students to theories and methodologies in the interdisciplinary field of feminist science studies. Specific areas of investigation include scientific cultures, animal models, and science in the media and popular culture. Students will continuously engage larger questions such as: What kinds of knowledge count as "science?" What is objectivity? How have cultural assumptions shaped scientific knowledge production in this and other historical periods? What is the relationship between "the body" and scientific data?

This course introduces students to issues involved in the social and historical construction of gender identities and roles from a cross-cultural and interdisciplinary perspective. Topics, which change from year-to-year, have included gender and sexuality; the uses and limits of biology in explaining gender differences; women’s participation in production and reproduction; the intertwining of gender, race, nationality, and class in explaining oppression and resistance; women, men and globalization; and gender and warfare.

Our world is awash in data. To allow decisions to be made based on evidence, there is a need for statisticians to be able to make sense of the data around us and communicate their findings. In this course, students will be exposed to advanced statistical methods and will undertake the analysis and interpretation of complex and real-world datasets that go beyond textbook problems.

Our world is awash in data. To allow decisions to be made based on evidence, there is a need for statisticians to be able to make sense of the data around us and communicate their findings. In this course, students will be exposed to advanced statistical methods and will undertake the analysis and interpretation of complex and real-world datasets that go beyond textbook problems.

Fall and spring semesters. The Department.

(Offered as STAT 360 and MATH 360) This course explores the nature of probability and its use in modeling real world phenomena. There are two explicit complementary goals: to explore probability theory and its use in applied settings, and to learn parallel analytic and empirical problem-solving skills. The course begins with the development of an intuitive feel for probabilistic thinking, based on the simple yet subtle idea of counting. It then evolves toward the rigorous study of discrete and continuous probability spaces, independence, conditional probability, expectation, and variance.

(Offered as STAT 360 and MATH 360) This course explores the nature of probability and its use in modeling real world phenomena. There are two explicit complementary goals: to explore probability theory and its use in applied settings, and to learn parallel analytic and empirical problem-solving skills. The course begins with the development of an intuitive feel for probabilistic thinking, based on the simple yet subtle idea of counting. It then evolves toward the rigorous study of discrete and continuous probability spaces, independence, conditional probability, expectation, and variance.

Statistical Communication is an important component of the capacity to "think with data." This course will integrate theoretical and practical aspects of statistics with a focus on communicating results and their implications. Students will gain experience clearly synthesizing and explaining complex data using diverse predictive and explanatory models.

Making sense of a complex, high-dimensional data set is not an easy task. The analysis chosen is ultimately based on the research question(s) being asked. This course will explore how to visualize and extract meaning from large data sets through a variety of analytical methods. Methods covered include principal components analysis and selected statistical and machine learning techniques, both supervised (e.g. classification trees and random forests) and unsupervised (e.g. clustering).