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This course examines applications of Data Science in the decarbonization of energy systems. The course covers 1) basic energy systems concepts and background with US and global examples, 2) an introduction to relevant methods in statistical and geospatial data analytics and machine learning, and 3) trends and challenges affecting decarbonization in the electricity sector and beyond, with a focus on end-uses of energy.

This course examines applications of Data Science in the decarbonization of energy systems. The course covers 1) basic energy systems concepts and background with US and global examples, 2) an introduction to relevant methods in statistical and geospatial data analytics and machine learning, and 3) trends and challenges affecting decarbonization in the electricity sector and beyond, with a focus on end-uses of energy.

Introduction to core machine learning models and algorithms for classification, regression, dimensionality reduction and clustering. The course will cover the mathematical foundations behind the most common machine learning algorithms, and the effective use in solving real-world applications. Requires a strong mathematical background and knowledge of one high-level programming language such as Python.

Introduction to core machine learning models and algorithms for classification, regression, dimensionality reduction and clustering. The course will cover the mathematical foundations behind the most common machine learning algorithms, and the effective use in solving real-world applications. Requires a strong mathematical background and knowledge of one high-level programming language such as Python.