Data-Driven Climate Science

Catalog description

This course explores the application of data-driven techniques in climate science, focusing on statistical methods, machine learning, and data assimilation. Students will learn how to analyze large climate datasets, extract meaningful patterns, and develop predictive models. Through hands-on exercises, we will cover topics such as observational data processing, feature selection, and neural networks for climate prediction. The course is designed for students with an interest in leveraging data science to address key challenges in climate research.