Climate Data Science: Predicting Extreme Weather with Machine Learning

Climate change is increasing the frequency and intensity of extreme weather events, making accurate environmental predictions more critical than ever. This text-based course introduces you to the intersection of climate science and data science, showing you how to leverage modern machine learning techniques to analyze environmental patterns. You will transition from understanding basic climate variables to building predictive models that can identify anomalies, heatwaves, and extreme precipitation events. Through clear written explanations and structured code walk-throughs, you will gain the practical skills needed to work with real-world meteorological data. What you'll learn: Understand foundational climate science concepts, terminology, and the structure of environmental datasets; Process and clean large-scale climate data using Python libraries such as xarray and pandas; Apply supervised machine learning algorithms to classify and predict extreme weather events; Address class imbalance issues inherent in modeling rare, extreme climate occurrences; Evaluate model performance using meteorologically relevant validation metrics. The course begins with core definitions of climate variables and data formats before guiding you through exploratory data analysis. You will then progress to implementing classification and regression algorithms on historical weather records to predict future anomalies. This course is designed for aspiring data scientists, environmental researchers, and programming beginners interested in climate tech, requiring only a basic familiarity with Python. Start analyzing climate patterns and building predictive models today.