1.715 Environmental Data Analysis
Covers probabilistic concepts and techniques that are useful for environmental data analysis. Topics include random variables, hypothesis testing, linear regression, analysis of trends, space-time domain analysis, frequency domain analysis, simulation of random fields, Markovian processes, derived distributions, and stochastic differential equations. Problem sets emphasize environmental applications.
This class has 1.010 as a prerequisite.
1.715 will not be offered this semester. It will be instructed by E. A. B. Eltahir.
This class counts for a total of 12 credits. This is a graduate-level class.
You can find more information at the Subjects site.
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