- Familiarity with basic descriptive statistics, differential/integral calculus (MATH140)
This is a comprehensive introductory course designed to prepare students to apply scientific computation and visualization techniques in Python to data intensive questions in the Natural Sciences. The class emphasizes real-world applications, providing students with essential hands-on experience using Python for data analysis and visualization, developing analytical skills for observational and modeling data, and performing virtual experiments to distinguish data contributing factors. Students will also master the command line Linux environment. Topics will include text editing, directory structure, permissions, file transfer techniques, shell scripting, and data archiving.
This course has three educational goals:
- Python programming centered around scientific data analysis and visualization.
- Working with real world data sets, including the challenges real data presents.
- Mastering command line linux. Topics to include remote server access, text editing, directory structure, permissions, file transfer techniques, shell scripting, and data archiving.
These goals will be bridged with homework plus exercise assignments utilizing both mathematical and programming skills to examine Earth’s climate data, both observed and modeled, accessible to the public. The analysis and programing skills learned can be more generally applied to other scientific data with variations in time and/or space. Students will use climate change data to explore signal vs noise, trend vs periodicity, natural vs anthropogenic forcing, local vs remote response, mean vs extreme changes, and accuracy vs uncertainty.