Course Description:
The interdisciplinary introduction to environmental big data. Focus is placed upon modeling and analyzing big data in built-in environments.

Course Objectives:
The objectives of the course are (1) to introduce basic knowledge of environmental big data for environmental scientists and engineers, (2) to provide a learning experience of advanced statistical techniques to analyze environmental big data, and (3) to demonstrate the analysis and modeling for natural-human coupled systems.

i) Programming and Problem Solutions (CSED101)
ii) or Permission of Instructor is Required.

In-class Assignments 10%
Classroom Participation 10%
Writing Assignments/Problem Sets 30%
Term Project 50%
Total 100%

Wilks, D.S., 2011. Statistical methods in the atmospheric sciences (Vol. 100). Academic press.

Other references will be provided in class.

1st week: Introduction to Environmental Big data
2nd week: Climate Data
3rd week: Water Quantity Data
4th week: Water Quality Data
5th week: Air Quality Data
6th week: Introduction to Linear Data Analysis
7th week: Empirical Orthogonal Function Analysis
8th week: Singular Value Decomposition Analysis
9th week: Neural Network Analysis
10th week: Nonliear EOF and SVD
11th-12th week: Practices for Statistical Techniques
13th-14th week: Visual Tool: QGIS
15th-16th week: Term Project Presentations

-Lecture Schedule Type
-Face-to-Face Instructional Method