BIOEE 3550

Data Analysis and Visualization in Ecology and Environmental Science

Course Description

Course information provided by the 2024-2025 Catalog. Courses of Study 2024-2025 is scheduled to publish mid-June.

Ecology and Environmental Science are running into a 'big data' era. The unprecedented data sources provide opportunities for novel scientific exploration and solutions to real-world problems, which, however, usually requires robust quantitative analysis and informative visualization. This course aims to increase students' literacy and hands-on skills on common quantitative methods in ecology and environmental sciences, including accessing and curating data, statistical inference, regression, data-based predictions (also known as machine learning), and visualizing the results. Students will be using public data sets from organismal to landscape scales, including spatial data sets from the Google Earth Engine platform. Example codes will be provided in both Python and R.

Prerequisites/Corequisites Prerequisite: Introductory Calculus and Statistics, BIOEE 1610 or equivalent, or permission of instructor.

Last 4 Terms Offered 2025SP, 2024SP, 2023SP

Outcomes

• Demonstrate quantitative reasoning and computational thinking skills over heterogenous data sets.
• Contrast motivation, theoretical basis, limitation, and applicable scenarios for common statistical inference and machine learning methods.
• Compare and evaluate different quantitative models to explain realistic ecological/environmental questions.
• Design and conduct scientific visualization on quantitative analysis results in Python/R.
• Access and analyze public spatial environmental data set on Google Earth Engine.

When Offered Spring.

Comments Recommended prerequisite: experience in Python/R.

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