Sequential decision analytics is at the heart of modern operations research, finance, and business strategy. This course is designed to give you both the theoretical foundations and the practical skills to model, analyze, and solve complex sequential decision-making problems using Python, Java, and Julia.

We begin with the fundamentals of Markov decision processes (MDPs), stochastic dynamic programming, and reinforcement learning, building a unified framework for modeling uncertainty and adaptivity in decision problems. From there, you will apply these methods to real-world scenarios across multiple domains:

• Dynamic Inventory Management – learn how to balance stock levels, demand uncertainty, and holding costs in supply chain systems.

• Adaptive Market Planning – explore strategies for responding to competitive and volatile markets with data-driven decision rules.

• Portfolio Management – apply sequential optimization techniques to allocate capital under risk and return trade-offs.

• Airline Pricing and Revenue Management – discover how dynamic pricing models maximize revenue in industries with fluctuating demand.

Throughout the course, you will work on hands-on projects implemented in Python, Java, and Julia, giving you exposure to multiple programming environments widely used in academia and industry. Each project is carefully designed to bridge theory with practice, ensuring that you not only understand the algorithms but can also implement them in real-world applications.

By the end of this course, you will be equipped with the tools and intuition to design intelligent decision-making systems across logistics, finance, marketing, and operations. This is a perfect course for engineers, data scientists, operations researchers, and anyone who wants to master the science of making optimal sequential decisions.