This course introduces sequential decision analytics -- the study of how to make a series of decisions over time when outcomes are uncertain and new information becomes available. It focuses on real-world business problems where uncertainty and risk shape decision-making. Students will learn how to structure and solve problems where decisions are made over time, new information unfolds dynamically, and uncertainty plays a central role. Topics include decision trees, dynamic programming, policy design, simulation, modeling and managing risk, and applications in areas such as operations, finance, digital markets, and innovation. The course emphasizes developing decision models that are both analytically rigorous and practically relevant. Students will use a general-purpose programming language to implement solutions and evaluate different decision strategies.