Global Arc

After the designation of Mexican drug cartels as Foreign Terrorist Organizations by the United States, it has become urgent to ask: how have Mexican drug cartels become an essential part of Mexico's governmentality, while also being perceived as a threat to other countries? In this course, we will address this question by arguing, both philosophically and historically, that Mexican narcos are neither mere criminals nor terrorist, but rather micro-sovereigns. Some of the thinkers and artists who will help us understand the concept of micro-sovereignty include Thomas Hobbes, Michel Foucault, Oswaldo Zavala, Luis Estrada and Mónica Ojeda.

The lie is as old as language, but reportage suggests something singularly dishonest about the present moment. Accusations of "fake news!" abound; journalism is threatened by censorship; AI plagiarism beleaguers universities; governments routinely use "truth commissions" to reckon with atrocity, even as those affected critique them as insincere. This seminar asks how we differentiate heretics and bluffers, kind-hearted tellers of white lies, conspiracy theorists and con artists. Is a falsehood uttered with conviction a lie? Is deceit ever justified? How do evidentiary regimes shape what knowledge is received as truth and what is disqualified?

Political campaigns in the 2020s are becoming more technology dependent, more confusing and difficult from a communications perspective and even more fraught with pitfalls and personal challenges. When deciding whether to run for any office all of these issues must be confronted in addition to the core considerations of issue positions, fundraising and the most important question of all: If I do win, what do I want to accomplish and what kind of leader do I want to be?

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 methods of American Studies, Asian American Studies, and Latino Studies through discussion of some of the signature ideas, events, and debates in and about America's past and present. It presents students various scholarly approaches to 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.