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91 - 100 of 119 results for: all courses

PHYSICS 105: Intermediate Physics Laboratory I: Analog Electronics

Analog electronics including Ohm's law, passive circuits and transistor and op amp circuits, emphasizing practical circuit design skills to prepare undergraduates for laboratory research. Short design project. Minimal use of math and physics, no electronics experience assumed beyond introductory physics. Prerequisite: PHYSICS 43 or PHYSICS 63.
Terms: Aut | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA | Grading: Letter or Credit/No Credit

PHYSICS 107: Intermediate Physics Laboratory II: Experimental Techniques and Data Analysis

Experiments on lasers, Gaussian optics, and atom-light interaction, with emphasis on data and error analysis techniques. Students describe a subset of experiments in scientific paper format. Prerequisites: completion of PHYSICS 40 or PHYSICS 60 series, and PHYSICS 70 and PHYSICS 105. Recommended pre- or corequisites: PHYSICS 120 and 130. WIM
Terms: Win | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA | Grading: Letter or Credit/No Credit

PHYSICS 108: Advanced Physics Laboratory: Project

Small student groups plan, design, build, and carry out a single experimental project in low-temperature physics. Prerequisites PHYSICS 105, PHYSICS 107.
Terms: Win, Spr | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA | Grading: Letter or Credit/No Credit

PHYSICS 113: Computational Physics

Numerical methods for solving problems in mechanics, electromagnetism, quantum mechanics, and statistical mechanics. Methods include numerical integration; solutions of ordinary and partial differential equations; solutions of the diffusion equation, Laplace's equation and Poisson's equation with relaxation methods; statistical methods including Monte Carlo techniques; matrix methods and eigenvalue problems. Short introduction to MatLab, used for class examples; class projects may be programmed in any language such as C. Prerequisites: MATH 53 and PHYS 120. Previous programming experience not required.
Terms: Aut | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

POLISCI 101: Introduction to International Relations

(Formerly POLISCI 1) Approaches to the study of conflict and cooperation in world affairs. Applications to war, terrorism, trade policy, the environment, and world poverty. Debates about the ethics of war and the global distribution of wealth.
Terms: Aut | Units: 5 | UG Reqs: GER:DB-SocSci, WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit

POLISCI 104: Introduction to Comparative Politics

(Formerly POLISCI 4) Why are some countries prone to civil war and violence, while others remain peaceful? Why do some countries maintain democratic systems, while others do not? Why are some countries more prosperous than others? This course will provide an overview of the most basic questions in the comparative study of political systems, and will introduce the analytical tools that can help us answer them.
Terms: Spr | Units: 5 | UG Reqs: GER:DB-SocSci, GER:EC-GlobalCom, WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit

POLISCI 150A: Data Science for Politics (POLISCI 355A)

Data science is quickly changing the way we understand and and engage in the political process. In this course we will develop fundamental techniques of data science and apply them to large political datasets on elections, campaign finance, lobbying, and more. The objective is to give students the skills to carry out cutting edge quantitative political studies in both academia and the private sector. Students with technical backgrounds looking to study politics quantitatively are encouraged to enroll.
Terms: Aut | Units: 5 | UG Reqs: WAY-AQR | Grading: Letter (ABCD/NP)

POLISCI 150B: Machine Learning for Social Scientists (POLISCI 355B)

Machine learning---the use of algorithms to classify, predict, sort, learn and discover from data---has exploded in use across academic fields, industry, government, and non-profit. This course provides an introduction to machine learning for social scientists. We will introduce state of the art machine learning tools, show how to use those tools in the programming language R, and demonstrate why a social science focus is essential to effectively apply machine learning techniques. Applications of the methods will include forecasting social phenomena, the analysis of social media data, and the automatic analysis of text data. Political Science 150A or an equivalent is required. (Prerequisite 150A/355A)
Terms: Win | Units: 5 | UG Reqs: WAY-AQR | Grading: Letter or Credit/No Credit

POLISCI 155: Political Data Science (PUBLPOL 157)

Introduction to methods of research design and data analysis used in quantitative political research. Topics covered include hypothesis testing, linear regression, experimental and observational approaches to causal inference, effective data visualization, and working with big data. These topics will be introduced using data sets from American politics, international relations, and comparative politics. The course begins with an intensive introduction to the R programming language used throughout the course. Satisfies quantitative methods requirement for the Political Science Research Honors Track. Prerequisites: Stat 60 or instructor consent.
Terms: not given this year | Units: 5 | UG Reqs: WAY-AQR | Grading: Letter or Credit/No Credit

POLISCI 241S: Spatial Approaches to Social Science (ANTHRO 130D, ANTHRO 230D, URBANST 124)

This multidisciplinary course combines different approaches to how GIS and spatial tools can be applied in social science research. We take a collaborative, project oriented approach to bring together technical expertise and substantive applications from several social science disciplines. The course aims to integrate tools, methods, and current debates in social science research and will enable students to engage in critical spatial research and a multidisciplinary dialogue around geographic space.
Terms: Aut | Units: 5 | UG Reqs: WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit
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