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 science and engineering, (2) to provide a learning experience of artificial intelligence techniques to analyze environmental big data, and (3) to establish the analysis and modeling skill for environmental issues in natural-human coupled system.

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

Classroom Participation: 10%
In-class Quiz/Assignment: 20%
Term Project Presentation I: 10%
Term Project Presentation II: 15%
Final Project Presentation: 20%
Final Project Report: 25%
Total: 100%

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

Other references will be provided in class.

Week 1: Introduction to Environmental Big Data
Week 2: Introduction to Python Jupyter Notebook (I)
Week 3: Introduction to Pyhton Jupyter Notebook (II)
Week 4: Data Visualization in Python Jupyter Notebook
Week 5: Term Project Presentation (I)
Week 6: Deep Learning (I)
Week 7: Deep Learning (II)
Week 8: Machine Learning (I)
Week 9: Machine Learning (II)
Week 10: Term Project Presentation (II)
Week 11: Intro. to Data Mining: Open-API
Week 12: Intro. to Data Mining: Crawling
Week 13: Intro. to Python for Sentiment Analysis
Week 14: Sentiment Analysis: Climatic Extremes
Week 15: Final Term Project Presentation

-In-personal and Offline Lecture Schedule Type
-Face-to-Face Instructional Method