Data Analysis and Visualization in Ecology and Environmental Science

Catalog description

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.