Data Analysis For Earth System Science

In modern fields of geosciences, vast quantities of spatio-temporal data are often available to researchers. Our data are sparse, noisy, and typically unlabelled. This course will introduce broad and general analysis tools that can be applied to the student’s research. We will discuss case studies from both the earth- and atmospheric-science literature. The focus is on understanding common pitfalls and best practices of data analysis in geosciences. Towards the end of this course, the students will be able to select the best model for the task at hand and learn the importance of properly quantifying the uncertainty in their conclusions.

References

• Menke, William. Geophysical data analysis: Discrete inverse theory. Academic press, 2018.
• Tarantola, Albert. Inverse problem theory and methods for model parameter estimation. Society for industrial and applied mathematics, 2005.
• Robinson, Enders A., and Sven Treitel. Geophysical signal analysis. Society of Exploration Geophysicists, 2000
• A. Blum, J. Hopcroft, and R. Kannan (2020) Foundations of Data Sciences, Cambridge University Press
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: Springer.
• Boyd, Stephen, and Lieven Vandenberghe. Introduction to applied linear algebra: vectors, matrices, and least squares. Cambridge university press, 2018.