15.780 Stochastic Models in Business Analytics
Introduces core concepts in data-driven stochastic modeling that inform and optimize business decisions under uncertainty. Covers stochastic models and frameworks, such as queuing theory, time series forecasting, network models, dynamic programming, and stochastic optimization. Draws on real-world applications, with several examples from retail, healthcare, logistics, supply chain, social and online networks, and sports analytics.
Lecture occurs 11:30 AM to 1:00 PM on Mondays and Wednesdays in E62-250.
This class counts for a total of 12 credits.
You can find more information at the 15.780 Class Site site.
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