Ecological and Environmental Data Analytics
Ecology and Environmental Science have entered an era of increasing data volume and complexity, demanding rigorous quantitative reasoning and effective data communication. This course advances data literacy of undergraduate and graduate students by covering theoretical foundations and practical coding skills to analyze, interpret, and present ecological and environmental data. Through integrated lectures and laboratory sessions, students will gain knowledge and hands-on experience for data curation, hypothesis testing, regression modeling, and visualization. The course also introduces new machine learning and AI tools for ecological data analytics. Students will develop proficiency in Python and R using real-world data sets spanning organismal to landscape scales. Designed for students in biology and environmental science, this course builds a rigorous, modern quantitative toolkit for ecological research and environmental problem-solving.
Topics
- Identify and differentiate common goals of ecology and environmental data analytics.
- Explain motivation, theoretical basis, and limitation for statistical inference and machine learning methods when analyzing ecological and environmental data.
- Access, organize, and analyze tabular data related to realistic ecological/environmental questions in Python or R
- Design and produce scientific visualization on quantitative analysis results.
- Graduate students are expected to practice and enhance project design skills for their own research subjects.
Official roster learning outcomes
Prerequisites & course information
background equivalent to: MATH 1106 or MATH 1110; STSCI 2100 or STSCI 2150; BIOEE 1610; or equivalents or permission of instructor.
Sources
- Course catalogue ↗2026-2027