This is the first of a four-part tutorial on sequential decision analytics. The tutorial will start by describing the broad class of sequential decision problems, and then introduce the universal modeling framework that can be used to model *any* sequential decision problems. The objective is to find effective policies for making decisions. I describe four classes of policies that span *any* method for making decisions over time. These four classes are illustrated using a variety of application settings. I then close by proposing that a new field, "sequential decision analytics" should be taught. Outlines of different courses are given at https://tinyurl.com/RLSOcourses/
