Global Arc

A six-week intensive introduction to Attic Greek as written and spoken in 5th cent. BCE Athens. This course is equivalent to CLG101/102, and will allow you to enroll in CLG105 in the Fall. Students can expect daily assignments and quizzes, practice with reading Greek and a brisk pace through ancient Greek grammar and syntax.

Data-driven decision-making, research discovery, and technology development are everywhere. It is now more important than ever for individuals to understand how data are used for these purposes. This course will introduce the student to how statistical reasoning and methods are used to learn from and leverage modern data. The emphasis will be on concepts and strategies for learning from data, rather than on sophisticated mathematics. Students will be exposed to the basics of statistics, machine learning, and data science through real world problems and applications. Students will also analyze data sets using the computer.

Introduction to Data Science provides a practical introduction to the burgeoning field of data science. The course introduces students to the essential tools for conducting data-driven research, including the fundamentals of programming techniques and the essentials of statistics. Students will work with real-world datasets from various domains; write computer code to manipulate, explore, and analyze data; use basic techniques from statistics and machine learning to analyze data; learn to draw conclusions using sound statistical reasoning; and produce scientific reports. No prior knowledge of programming or statistics is required.

This course provides the training for students to be independent in modern data analysis. The course emphasizes the rigorous treatment of data and the programming skills and conceptual understanding required for dealing with modern datasets. The course examines data analysis through the lens of statistics and machine learning methods. Students verify their understanding by working with real datasets. The course also covers supporting topics such as experiment design, ethical data use, best practices for statistical and machine learning methods, reproducible research, writing a quantitative research paper, and presenting research results.

Project-based course in which students work individually or in small teams to tackle data science and ML problems based on real datasets. We will emphasize critical thinking about experiments and large dataset analysis along with the ability to clearly communicate one's research. This course is intended to support students in developing the analytical skills necessary for quantitative independent work; students should consult with their home department about how this course could appropriately complement, but not replace, their independent work requirements.

Project-based course in which students work individually/small teams to tackle DS and ML problems, working with real-world datasets.The course emphasizes critical thinking about experiments and dataset analysis and the ability to clearly communicate one's research. Programming components are taught in Python. Experience in only one of the two programming languages (R and Python) is required.This course is intended to support students in developing the analytical skills for quantitative independent work; students should consult with their home department about how this course could complement, not replace, their independent work requirements.

In this seminar, we will explore the pedagogy of introductory data science. Students in the seminar will be required to work as undergraduate course assistants in SML 201 -- Introduction to Data Science. SML 201 topics will be discussed in more depth in the seminar, with a view of teaching the basic material. We will discuss literature in the pedagogy of computer science and statistics. Discussion topics will include teaching programming using the functional programming paradigm, the design of the dplyr package, simulation-based inference, teaching statistics using simulation-based inference, the grammar of graphics, and causal inference.

This course introduces students to the subjects of American Studies through discussion of some of the signature ideas, events, and debates in America's past and present in order to understand America as it exists today. It examines both historical and mythic manifestations of America from local, national, and global perspectives and considers the historical and cognitive processes associated with the delineation of America. The course examines a wide range of material and media from the point of view of multiple fields of study, and it engages the voices of diverse individuals and cultures in telling the story of America then and now.

This course engages critically with the artifacts and archives of contemporary American culture, inviting students to view, read, and create these artifacts with an eye toward what they tell us about how the United States represents itself and its citizens through various genres including theatre, musicals, film, TV, music, graphic and written novels, games, and the internet. Who are the heroes and villains in contemporary U.S. pop culture? How are family, work, and romance represented across races, genders and sexualities? How are economics and social class portrayed? Do the narratives we consume still promise an American Dream?

Modern baseball is a complex game, an international business, and a social and cultural touchstone. Combining a close study of the game¿s past with a thorough analysis of its present, the course examines baseball¿s evolution into the sport and industry it has become today. Topics include race and ethnicity (the breaking of the color line), labor and economics (the advent of free agency), globalization (the international game), geography (expansion and franchise relocation), architecture and public policy (stadium design and funding), as well as community and culture (journalism, statistical analysis). One three-hour seminar.