printer friendly pagePHYSICS 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
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
Instructors:
Schleier-Smith, M. (PI)
;
Goldman, H. (TA)
;
Ng, E. (TA)
...
more instructors for PHYSICS 107 »
Instructors:
Schleier-Smith, M. (PI)
;
Goldman, H. (TA)
;
Ng, E. (TA)
;
Rider, A. (TA)
;
Tan, S. (TA)
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-SMA, WAY-AQR
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
Instructors:
Abel, T. (PI)
;
Nosarzewski, B. (TA)
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
Instructors:
Fearon, J. (PI)
;
Buch, B. (TA)
;
Esberg, J. (TA)
...
more instructors for POLISCI 101 »
Instructors:
Fearon, J. (PI)
;
Buch, B. (TA)
;
Esberg, J. (TA)
;
Grinberg, M. (TA)
;
Hundley, L. (TA)
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: WAY-AQR, GER:DB-SocSci, WAY-SI, GER:EC-GlobalCom
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
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
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.
Last offered: Autumn 2014
| UG Reqs: WAY-AQR
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
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