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131 - 140 of 150 results for: all courses

PSYCH 10: Introduction to Statistical Methods: Precalculus (STATS 60, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

PUBLPOL 104: Economic Policy Analysis (ECON 150, PUBLPOL 204)

The relationship between microeconomic analysis and public policy making. How economic policy analysis is done and why political leaders regard it as useful but not definitive in making policy decisions. Economic rationales for policy interventions, methods of policy evaluation and the role of benefit-cost analysis, economic models of politics and their application to policy making, and the relationship of income distribution to policy choice. Theoretical foundations of policy making and analysis, and applications to program adoption and implementation. Prerequisites: ECON 50 and ECON 102B. Undergraduate Public Policy students are required to take this class for a letter grade and enroll in this class for five units.
Terms: Win | Units: 4-5 | UG Reqs: WAY-AQR | Grading: Letter or Credit/No Credit

PUBLPOL 105: Empirical Methods in Public Policy (PUBLPOL 205)

Methods of empirical analysis and applications in public policy. Emphasis on causal inference and program evaluation. Public policy applications include health, education, and labor. Assignments include hands-on data analysis, evaluation of existing literature, and a final research project. Objective is to obtain tools to 1) critically evaluate evidence used to make policy decisions and 2) perform empirical analysis to answer questions in public policy. Prerequisite: ECON 102B. Enrollment is limited to Public Policy students. Public Policy students must take the course for a letter grade.
Terms: Win, Spr | Units: 4-5 | UG Reqs: WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit

PUBLPOL 157: Political Data Science (POLISCI 155)

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, last offered Autumn 2014 | Units: 5 | UG Reqs: WAY-AQR | Grading: Letter or Credit/No Credit

SINY 150: Biology, Technology, and Society: The City as a Human Life Support System

While environmental issues related to cities are often considered in the context of climate change, this course will use New York City as a lab to explore how dense global cities deal with their intense biological needs clean drinking water, sanitation and sewage, public health, food supply the ongoing management and maintenance of which occupy a surprising portion of the infrastructure, management, and tax expenditure of most city governments.
Terms: Spr | Units: 4 | UG Reqs: WAY-AQR | Grading: Letter or Credit/No Credit

SOC 180B: Introduction to Data Analysis (CSRE 180B, SOC 280B)

Methods for analyzing and evaluating quantitative data in sociological research. Students will be taught how to run and interpret multivariate regressions, how to test hypotheses, and how to read and critique published data analyses.
Terms: Spr | Units: 4 | UG Reqs: GER:DB-SocSci, WAY-AQR, WAY-SI | Grading: Letter (ABCD/NP)

STATS 48N: Riding the Data Wave

Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To an more »
Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.
Terms: not given this year, last offered Autumn 2016 | Units: 3 | UG Reqs: WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit

STATS 101: Data Science 101

http://web.stanford.edu/class/stats101/ . This course will provide a hands-on introduction to statistics and data science. Students will engage with the fundamental ideas in inferential and computational thinking. Each week, we will explore a core topic comprising three lectures and two labs (a module), in which students will manipulate real-world data and learn about statistical and computational tools. Students will engage in statistical computing and visualization with current data analytic software (Jupyter, R). The objectives of this course are to have students (1) be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis, and (2) be knowledgeable about programming abstractions so that they can later design their own computational inferential procedures. No programming or statistical background is assumed. Freshmen and sophomores interested in data science, computing and statistics are encouraged to attend. Open to graduates as well.
Terms: Aut, Spr, Sum | Units: 5 | UG Reqs: GER: DB-NatSci, WAY-AQR | Grading: Letter or Credit/No Credit

STATS 110: Statistical Methods in Engineering and the Physical Sciences

Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus.
Terms: Aut, Sum | Units: 4-5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR | Grading: Letter or Credit/No Credit
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