ENVIRONMENTAL DATA SCIENCE

Prerequisites & course information

Requisites: STAT 240 , 301 , 324 , 371 , or graduate/professional standing Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Level - Intermediate L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Demonstrate introductory skills in using collaboration technology (e.g. Jupyter Notebooks) to write, edit, and run programs in a scientific programming language (e.g. Python) Audience: Both Grad & Undergrad 2. Recognize, read, write and use common environmental dataset formats Audience: Both Grad & Undergrad 3. Use a scientific programming language (e.g. Python) to read and process environmental data Audience: Both Grad & Undergrad 4. Produce visualizations of environmental data, including basic scientific charts, statistics, and maps Audience: Both Grad & Undergrad 5. Understand fundamentals of modern machine learning algorithms and gain experience of practical use of them Audience: Both Grad & Undergrad 6. Solve real-world data science problems individually and in teams Audience: Both Grad & Undergrad 7. Identify the frontiers in real-world environmental science challenges and how data science can help Audience: Both Grad & Undergrad 8. Identify a problem in environmental science that may be solved or better understood through data science, provide a basic visualization or analysis of a data set associated with that problem, and communicate findings effectively Audience: Undergraduate 9. Develop in-depth spatial and/temporal analyses using advanced data science tools (such as machine learning) and visual datasets related to your own research or anticipated area of research that meet the standards of scientific journals, critically evaluate your findings, and situate them within the larger context of current research literature Audience: Graduate Learning Outcomes: 1. Demonstrate introductory skills in using collaboration technology (e.g. Jupyter Notebooks) to write, edit, and run programs in a scientific programming language (e.g. Python) Audience: Both Grad & Undergrad 2. Recognize, read, write and use common environmental dataset formats Audience: Both Grad & Undergrad 3. Use a scientific programming language (e.g. Python) to read and process environmental data Audience: Both Grad & Undergrad 4. Produce visualizations of environmental data, including basic scientific charts, statistics, and maps Audience: Both Grad & Undergrad 5. Understand fundamentals of modern machine learning algorithms and gain experience of practical use of them Audience: Both Grad & Undergrad 6. Solve real-world data science problems individually and in teams Audience: Both Grad & Undergrad 7. Identify the frontiers in real-world environmental science challenges and how data science can help Audience: Both Grad & Undergrad 8. Identify a problem in environmental science that may be solved or better understood through data science, provide a basic visualization or analysis of a data set associated with that problem, and communicate findings effectively Audience: Undergraduate 9. Develop in-depth spatial and/temporal analyses using advanced data science tools (such as machine learning) and visual datasets related to your own research or anticipated area of research that meet the standards of scientific journals, critically evaluate your findings, and situate them within the larger context of current research literature Audience: Graduate