Modeling and Predicting Climate Anomalies Specialization

In this specialization, you’ll gain a comprehensive foundation in climate change policies, statistical modeling, and machine learning, all applied to the context of global climate challenges. You will learn how to critically evaluate climate policies, analyze climate data using Python, and leverage machine learning to predict extreme weather behaviors. With a focus on real-world applications you'll develop practical skills to interpret and model climate data to address one of the most pressing issues of our time. Whether you’re a data scientist, climate researcher, or policy advocate, this specialization provides a hands-on approach to mastering the tools and concepts that can help mitigate and adapt to the impacts of climate change. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Applied Learning Project Throughout the specialization, you'll work on real-world projects that involve accessing climate data through API calls, analyzing and visualizing trends, and applying machine learning models. By the end of the specialization, you'll practice selecting a region of interest, collecting and cleaning climate data, and using advanced analytical techniques to model and predict climate anomalies.