ATMS 523

Credit: 4 hours.

Develops real-world hands-on experience with a broad range of data analysis tools that are currently being used in academic, national laboratories, consulting, and private industry. Data sources in the atmospheric sciences are diverse and require specialized tools to open and reduce those datasets in an efficient manner. Focuses on preparation to become a developer of data analysis tools in collaborative research environments in a variety of professional settings. Provides skills, tools, and best practices to discover and cite earth science datasets, curate those sources and code developed, and enable reproducibility of the workflow to allow for transparency, open peer-review, and extension of the work.

4 graduate hours. No professional credit. Prerequisite: ATMS 517 or equivalent Python experience or consent of instructor.

Students must register for one lab and one lecture section.