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101 - 110 of 131 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
Instructors: Fox, J. (PI)

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
Instructors: Hollberg, L. (PI)

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, astrophysics, 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 various methods; statistical methods including Monte Carlo techniques; matrix methods and eigenvalue problems. Short introduction to Python, used for class examples; class projects may be programmed in any language such as C, python or julia. No Prerequisites. Previous programming experience not required.
Terms: Spr | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit
Instructors: Cabrera, B. (PI)

POLISCI 101: Introduction to International Relations

Approaches to the study of conflict and cooperation in world affairs. Applications to First and Second World Wars, the Cold War, terrorism, economic policy, and development.
Terms: Aut, Spr | 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
Instructors: Terman, R. (PI)

POLISCI 150C: Causal Inference for Social Science (POLISCI 355C)

Causal inference methods have revolutionized the way we use data, statistics, and research design to move from correlation to causation and rigorously learn about the impact of some potential cause (e.g., a new policy or intervention) on some outcome (e.g., election results, levels of violence, poverty). This course provides an introduction that teaches students the toolkit of modern causal inference methods as they are now widely used across academic fields, government, industry, and non-profits. Topics include experiments, matching, regression, sensitivity analysis, difference-in-differences, panel methods, instrumental variable estimation, and regression discontinuity designs. We will illustrate and apply the methods with examples drawn from various fields including policy evaluation, political science, public health, economics, business, and sociology. Political Science 150A and 150B or an equivalent is required.
Terms: Spr | 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
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