EAPS 202 Computational Data Analysis in Earth and Planetary Sciences

A part of the core curriculum required of all graduate students. The statistical analysis and interpretation of quantitative data is central to graduate research. Driven by increasing data availability, processing power, and model sophistication, this interpretation and analysis is increasingly computational in nature. This course aims to provide graduate students with a working knowledge of computational data analysis and statistics including (1) programmatically applying both Bayesian and Frequentist statistical methods to Earth and planetary science data; (2) an introduction to advanced statistical methods applicable to the Earth and planetary sciences, such as time series analysis and spatial data analysis (geostatistics); and (3) an introduction to some of the tools and best practices of programming and software engineering used to produce maintainable, reproducible, and scalable software for scientific computing.  Not open to undergraduate students.