Course:FRE527
| Environmental Data Analytics | |
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| FRE 527 | |
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| Instructor: | Dr. Josephine Gantois |
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| Office Hours: | TBA |
| Class Schedule: | Feb 26 to April 12
Tue&Thur 2:30-4 pm |
| Classroom: | MCML 154 |
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| Syllabus | |
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COURSE NUMBER and TITLE: FRE 527: Environmental Data Analytics
COURSE DESCRIPTION
This course will introduce you to data extraction, wrangling, processing, analysis, and visualization tools, which are particularly adapted to dealing with environmental data. The programming languages covered are mainly Python and some R. We will apply these data analytics tools to large and complex environmental datasets, such as weather data or satellite imagery, which have high relevance for the food and resource sector.
LEARNING OUTCOMES
- Write reusable Python and R scripts to extract, process, analyze, and visualize environmental data, including geospatial data.
- Experience with data acquisition and tools for big data analysis (e.g. using cloud-based services like Google Earth Engine)
- Develop critical thinking to assess evidence based on large or complex environmental data
ASSESSMENT REPORT
Short report and presentation of a group project: the goal of the project is to produce a visualization of environmental data, to answer a specific need identified by the group, and to document the full analytics pipeline, from data extraction to visualization.
BIG QUESTIONS & REAL-WORLD APPLICATIONS IN CLIMATE, FOOD AND THE ENVIRONMENT COVERED IN THE COURSE
- 1. How can publicly available environmental data be efficiently accessed, processed, and visualized? 2. What role can environmental data play in environmental policy enforcement? 3. What unique characteristics of environmental data and analytical goal are important to consider when choosing a programming tool?
ASSESSMENT METHODS:
2-3 home assignments; weekly lab assignments; in-class activities (think-pair-share); project idea submission, final report, and presentation.( Details: TBD)