Sequential decision problems arise in virtually every human process. They span finance, energy, transportation, health, e-commerce, and supply chains and include pure learning problems that arise in laboratory or field experiments. They even cover search algorithms to maximize uncertain functions. An important dimension of every problem setting is the need to make decisions in the presence of different forms of uncertainty and evolving information processes. The goal of this book is to enable readers to understand how to approach, model and solve a sequential decision problem. To that end, it uses a teach-by-example style to illustrate a modeling framework that can represent any sequential decision problem. The presentation should not require mathematics beyond what would be expected in a first course on probability and statistics. This said, the book is centered on showing how to describe sequential decision problems using notation that is precise enough that it can be the basis of computer software. Python modules accompany most of the chapters; these modules were written around the modeling framework that runs throughout the book. At the same time, any software package that simulates a sequential decision problem, regardless of how it is being solved, can be translated directly into the modeling framework we use. For this reason, we encourage readers to look at any piece of notation as a variable in a computer program.