Romantic writers were obsessed with uncertainty, ignorance, and the irrational, unthinking mind. Concerned with the unusual ideas that surface when we are sleeping or spaced out, absorbed or intoxicated, Romanticism embraced reason’s alternatives: forgetting, fragmentation, stupidity, and spontaneous, uncontrollable emotion. From Wordsworth’s suggestion that children are wiser than adults to Keats’s claim that great writers are capable of remaining uncertain without reaching for fact or reason, Romantic poets and novelists suggested that we have something to learn from not thinking.

The deadly fog that hung over London throughout the 19th century was both a social reality and a pungent metaphor for a metropolis in which it seemed that almost anything could be hidden: secrets, crimes, identities. But sometimes the fog parts—and then comes scandal. We'll begin with Dickens' anatomy of the city in Bleak House; move on to sensation novels by Wilkie Collins and Mary Elizabeth Braddon, which contest and subvert the period's gender roles; look at murder with Sherlock Holmes and Dr.

During the formative periods when the local and global forces simultaneously took actions in shaping Chinese civilization, the functions of images and objects, the approaches to things, and the discourses around art underwent significant shifts, not only responding to but also mapping out the “Chinese-ness” in visual and material culture.

Same as PSY 364. Research on intergroup relationships and an exploration of theoretical and statistical models used to study mixed interpersonal interactions. Example research projects include examining the consequences of sexual objectification for both women and men, empathetic accuracy in interracial interactions, and gender inequality in household labor. A variety of skills including, but not limited to, literature review, research design, data collection, measurement evaluation, advanced data analysis, and scientific writing will be developed.

An introduction to data science using Python, R and SQL. Students learn how to scrape, process and clean data from the web; manipulate data in a variety of formats; contextualize variation in data; construct point and interval estimates using resampling techniques; visualize multidimensional data; design accurate, clear and appropriate data graphics; create data maps and perform basic spatial analysis; and query large relational databases. No prerequisites, but a willingness to write code is necessary. Enrollment limit of 30. (E)

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.

Same as SDS 291. Formerly MTH 247. 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: PSY 201, GOV 190, MTH 220, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination. Enrollment limited to 25.

Same as MTH 291. Formerly MTH 247. 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: MTH 201/PSY 201, GOV 190, MTH 219, MTH 220, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination.

Same as SDS 220. An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. MTH 220 satisfies the basis requirement for biological science, engineering, environmental science, neuroscience and psychology.

Same as MTH 220. (Formerly MTH 245). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data.