Topics course. The historical roles of slaves within the social and economic fabric of classical Greece and Rome. The scope and limits of ancient evidence in literary and artistic representations, as well as modern interpretive comparisions with other slave societies. Critical examination of concepts such as class, social mobility, social order, and status, along with gender and ethnicity.

The emergence of the Greek world from the Dark Age to Philip II of Macedon, c. 800?336 B.C.E., focusing on the politics, society and culture of late archaic and classical Greece. Main topics include colonization, tyranny, hoplites and city-state society; the Persian Wars; Sparta and Athens; Athenian empire and democracy; the rise of Macedon.

This course examines global artistic tendencies since 1945, in their art-historical and socio-historical contexts. The class considers such developments as American abstraction and the rise of New York, neo-dada, pop, minimalism, conceptual art, earthworks, the influence of feminism, postmodernism, the politics of identity, conceptions of the site and the institution, global publics and the global culture of art, and the theoretical issues and debates that help to frame these topics. Group B

Topics in statistics and data science. Statistical methods for analyzing data must be chosen appropriately based on the type and structure of the data being analyzed. The particular methods and types of data studied this in this course vary, but topics may include: categorical data analysis, time series analysis, survival analysis, structural equation modeling, survey methodology, Bayesian methods, resampling methods, spatial statistics, missing data methods, advanced linear models, statistical/machine learning, network science, relational databases, web scraping, and text mining.

Same as MTH 290. Note: This course is no longer considered the same as PSY 301, starting in the 2014?15 academic year. 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.

Same as MTH 246. An introduction to probability, including combinatorial probability, random variables, discrete and continuous distributions. Prerequisites: MTH 153 and MTH 212 (may be taken concurrently), or permission of the instructor. 

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.

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.

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.

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.