• Introduces core concepts and methods in data-driven modeling that inform and optimize decisions under uncertainty. Teaches modeling and computational skills (R and Python). Covers topics such as machine learning, time series forecasting, choice modeling, dynamic programming, mixed-integer programming, stochastic optimization, matching algorithms, and multi-armed bandits. Draws on real-world applications from retail, healthcare, logistics, supply chain, public sector, social applications, and online learning.

• Textbook Information

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