RESEARCH COMPUTING IN ATMOSPHERIC AND OCEANIC SCIENCES
Hands-on introduction to open-source research computing tools commonly used in the atmospheric and oceanic sciences. Covers Python basics, including standard data structures, environments, and packages that are useful for analyzing and visualizing a variety of observational and computational atmospheric and oceanic science datasets. Additional open-source tools for version control, sharing code, and collaborating on analysis are also introduced and open science best practice are discussed.
Prerequisites & course information
Requisites: ( MATH 234 and COMP SCI 220 ) or graduate/professional standing Course Designation: Core GenEd - Natural Science & Wellness + Lab Breadth - Physical Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Become familiar with computing tools that are useful in managing, analyzing, and visualizing atmospheric and oceanic science data, including Unix, git, and Python Audience: Both Grad & Undergrad 2. Understand multiple Python packages that are critical for atmospheric and oceanic science data analysis and use these packages to analyze and visualize atmospheric and oceanic science datasets Audience: Both Grad & Undergrad 3. Develop proficiency in the use of version control and other elements of open science best practices Audience: Both Grad & Undergrad 4. Implement a reproducible data analysis workflow that incorporates Python computing tools and version control and apply it to an unfamiliar dataset or new research topic Audience: Graduate Learning Outcomes: 1. Become familiar with computing tools that are useful in managing, analyzing, and visualizing atmospheric and oceanic science data, including Unix, git, and Python Audience: Both Grad & Undergrad 2. Understand multiple Python packages that are critical for atmospheric and oceanic science data analysis and use these packages to analyze and visualize atmospheric and oceanic science datasets Audience: Both Grad & Undergrad 3. Develop proficiency in the use of version control and other elements of open science best practices Audience: Both Grad & Undergrad 4. Implement a reproducible data analysis workflow that incorporates Python computing tools and version control and apply it to an unfamiliar dataset or new research topic Audience: Graduate
Sources
- Course catalogue ↗2026-2027