Many of the most pressing energy challenges of our time transcend traditional disciplinary boundaries and necessitate the use of systems models to analyze possible solutions. This course will show how methodological approaches can be applied to construct such energy systems models. Particular emphasis will be devoted to models that combine concepts from engineering, economics, natural sciences, and policy. The featured models will showcase a broad range of methodological approaches, such as optimization, simulation, machine learning, decision analysis, stochastic processes, and dynamical systems. More generally, the course will train students to build mathematical models that represent complex real-world energy system problems. Topics include basics of energy systems engineering, concepts in probability and statistics, spatial representation (e.g. GIS), and more. Recommend background knowledge in data analytics. Prerequisite: C or better in IE 3301 and REE 3301.