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This course provides students with a fundamental and practical understanding of data science and modeling approaches for environmental and agricultural systems analysis. The course is organized into three modules: (1) data retrieval, management, documentation, and visualization; (2) process-based modeling; and (3) data mining through statistical analysis and machine learning. Rather than develop a strong knowledge base in a specific methodology, students will gain broad and introductory understanding of a range of contemporary quantitative approaches and learn to think critically about the use of data analytics and models.

Prerequisite: Introductory statistics (e.g. ³§°ÕÌý515) and experience coding in R (e.g. µþ´¡·¡Ìý555)

Typically offered in Spring only