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41 - 50 of 131 results for: all courses

EARTHSYS 113: Earthquakes and Volcanoes (GEOPHYS 90)

Is the "Big One" overdue in California? What kind of damage would that cause? What can we do to reduce the impact of such hazards in urban environments? Does "fracking" cause earthquakes and are we at risk? Is the United States vulnerable to a giant tsunami? The geologic record contains evidence of volcanic super eruptions throughout Earth's history. What causes these gigantic explosive eruptions, and can they be predicted in the future? This course will address these and related issues. For non-majors and potential Earth scientists. No prerequisites. More information at: https://stanford.box.com/s/zr8ar28efmuo5wtlj6gj2jbxle76r4lu
Terms: Spr | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA | Grading: Letter or Credit/No Credit
Instructors: Beroza, G. (PI)

EARTHSYS 140: The Energy-Water Nexus (GEOPHYS 80)

Energy, water, and food are our most vital resources constituting a tightly intertwined network: energy production requires water, transporting and treating water needs energy, producing food requires both energy and water. The course is an introduction to learn specifically about the links between energy and water. Students will look first at the use of water for energy production, then at the role of energy in water projects, and finally at the challenge in figuring out how to keep this relationship as sustainable as possible. Students will explore case examples and are encouraged to contribute examples of concerns for discussion as well as suggest a portfolio of sustainable energy options.
Terms: alternate years, given next year | Units: 3 | UG Reqs: WAY-AQR | Grading: Letter or Credit/No Credit

EARTHSYS 141: Remote Sensing of the Oceans (EARTHSYS 241, ESS 141, ESS 241, GEOPHYS 141)

How to observe and interpret physical and biological changes in the oceans using satellite technologies. Topics: principles of satellite remote sensing, classes of satellite remote sensors, converting radiometric data into biological and physical quantities, sensor calibration and validation, interpreting large-scale oceanographic features.
Terms: Win | Units: 3-4 | UG Reqs: GER: DB-NatSci, WAY-AQR | Grading: Letter or Credit/No Credit
Instructors: Arrigo, K. (PI)

EARTHSYS 152: Marine Chemistry (EARTHSYS 252, ESS 152, ESS 252)

Introduction to the interdisciplinary knowledge and skills required to critically evaluate problems in marine chemistry and related disciplines. Physical, chemical, and biological processes that determine the chemical composition of seawater. Air-sea gas exchange, carbonate chemistry, and chemical equilibria, nutrient and trace element cycling, particle reactivity, sediment chemistry, and diagenesis. Examination of chemical tracers of mixing and circulation and feedbacks of ocean processes on atmospheric chemistry and climate. Designed to be taken concurrently with Biological Oceanography (EESS/ EARTHSYS 151/251)
Terms: Spr | Units: 3-4 | UG Reqs: WAY-AQR, WAY-SMA | Grading: Letter or Credit/No Credit

EARTHSYS 185: Feeding Nine Billion

Feeding a growing and wealthier population is a huge task, and one with implications for many aspects of society and the environment. There are many tough choices to be made- on fertilizers, groundwater pumping, pesticide use, organics, genetic modification, etc. Unfortunately, many people form strong opinions about these issues before understanding some of the basics of how food is grown, such as how most farmers currently manage their fields, and their reasons for doing so. The goal of this class is to present an overview of global agriculture, and the tradeoffs involved with different practices. Students will develop two key knowledge bases: basic principles of crop ecology and agronomy, and familiarity with the scale of the global food system. The last few weeks of the course will be devoted to building on this knowledge base to evaluate different future directions for agriculture.
Terms: Win | Units: 4-5 | UG Reqs: WAY-AQR | Grading: Letter or Credit/No Credit

ECON 45: Using Big Data to Solve Economic and Social Problems

This course will show how "big data" can be used to understand and solve some of the most important social and economic problems of our time. The course will give students an introduction to frontier research in applied economics and social science in a non-technical manner. Topics include equality of opportunity, education, income inequality, racial segregation, innovation and entrepreneurship, social networks, urban planning, health, crime, and political partisanship. In the context of these topics, the course will also provide a non-technical introduction to basic statistical methods and data analysis techniques, including regression analysis, causal inference, quasi-experimental methods, and machine learning. Optional sections will provide a more advanced treatment of these methods for interested students. Each week, the course will include a guest lecturer from a Silicon Valley firm or government agency who will discuss real-world applications of data science.
Terms: not given this year | Units: 4-5 | UG Reqs: WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit

ECON 102A: Introduction to Statistical Methods (Postcalculus) for Social Scientists

Probabilistic modeling and statistical techniques relevant for economics. Concepts include: probability trees, conditional probability, random variables, discrete and continuous distributions, correlation, central limit theorems, point estimation, hypothesis testing and confidence intervals for both one and two populations. Prerequisite: MATH 20 or equivalent.
Terms: Aut, Win | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit

ECON 102B: Applied Econometrics

Hypothesis tests and confidence intervals for population variances, chi-squared goodness-of-fit tests, hypothesis tests for independence, simple linear regression model, testing regression parameters, prediction, multiple regression, omitted variable bias, multicollinearity, F-tests, regression with indicator random variables, simultaneous equation models and instrumental variables. Topics vary slightly depending on the quarter. Prerequisites: Econ 102A or equivalent. Recommended: computer experience (course often uses STATA software to run regressions).
Terms: Win, Spr | Units: 5 | UG Reqs: WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit
Instructors: McKeon, S. (PI)

ECON 102C: Advanced Topics in Econometrics

The program evaluation problem. Identifying and estimating the effects of policies on outcomes of interest (e.g., tax rates on labor supply, etc.). Identifying and estimating the effects of human capital on earnings and other labor market outcomes. Topics: Instrumental variables estimation; limited dependent variable models (probit, logit, Tobit models); Panel data techniques (fixed and random effect models, dynamic panel data models); Duration models; Bootstrap and Estimation by Simulation. Prerequisite: Econ 102B
Terms: Win | Units: 5 | UG Reqs: WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit

ECON 125: Economic Development, Microfinance, and Social Networks

An introduction to the study of the financial lives of households in less developed countries, focusing on savings, credit, informal insurance, the expansion of microfinance, and social networks. Prerequisites- Econ 51 and 102B
Terms: Spr | Units: 5 | UG Reqs: GER:EC-GlobalCom, WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit
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