BIOEE 6550 - Data Analysis and Visualization in Ecology and Environmental Science

Spring. 3 credits. Letter grades only.

Prerequisite: Introductory Calculus and Statistics, BIOEE 1610 or equivalent, or permission of instructor. Recommended prerequisite: experience in Python/R. Co-meets with BIOEE 3550.

X. Xu.

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.

Outcome 1: Demonstrate quantitative reasoning and computational thinking skills over heterogenous data sets.

Outcome 2: Contrast motivation, theoretical basis, limitation, and applicable scenarios for common statistical inference and machine learning methods.

Outcome 3: Compare and evaluate different quantitative models to explain realistic ecological/environmental questions.

Outcome 4: Design and conduct scientific visualization on quantitative analysis results in Python/R.

Outcome 5: Access and analyze public spatial environmental data set on Google Earth Engine.

Outcome 6: Organize quantitative analysis into a report in the format of a typical research manuscript.