Course:FRE527

FRE 527: Environmental Data Analytics
FRE 527
Section:
Instructor:	Dr. Josephine Gantois
Email:	josephine.gantois@ubc.ca
Office:	MCML 237
Office Hours:	Tuesdays 4:00-5:00pm
Class Schedule:	Feb 24 to April 09 Tue&Thur 12:30 to 2:00 pm
Classroom:	MCML 154
Important Course Pages
	Syllabus
	Lecture Notes
	Assignments
	Course Discussion

COURSE DESCRIPTION

This course introduces you to core environmental datasets spanning weather, ecology, and satellite imagery data sources, which are routinely used to support environmental metrics and decision-making in the food and resource sector. It provides hands-on experience with data extraction, processing and analysis techniques, as well as visualization tools, which are particularly adapted to dealing with the complexities of each environmental data type. The programming languages covered are R and Python, as well as some JavaScript for Google Earth Engine

LEARNING OUTCOMES

By the end of this course, students will be able to:

Explain the current capacities and limitations for measuring a suite of core environmental variables
Exercise critical thinking when engaging with evidence based on environmental data
Identify appropriate sources of data given a particular analysis or visualization goal
Source multiple publicly available environmental datasets.
Write reusable R and Python scripts to extract, process, analyze, and visualize publicly available environmental data.
Manipulate large geospatial datasets using Google Earth Engine

REAL-WORLD APPLICATIONS IN CLIMATE, FOOD & ENVIRONMENT

How can publicly available environmental data be efficiently accessed, processed, and visualized?
What role can environmental data play in environmental policy enforcement?
What unique characteristics of environmental data and analytical goals are important to consider when choosing a programming tool?

ASSESSMENT


Activity		Percent of the Grades
Group Project	Question & data plan In-class presentation Final submission	5% 15% 15%
Weekly Quizzes		10%
Assignments	3 Assignments	45%
In-Class participation		10%
Total		100%