Prescriptive Data Science 101: Types of Problems
Welcome back to a series of articles on prescriptive data science! In my last post, I talked about descriptive, predictive, and prescriptive data science and required skillsets for prescriptive data science. Today, I will help your journey by defining different types of prescriptive data science problems.
Please note that the methods described in this article is called in many different names across multiple disciplines — mathematical optimization, control theory, single agent dynamics, multi agent dynamic game, dynamic programming, Markovian decision process, or reinforcement learning. In the subsequent article on key elements of problem definition for a single agent case, I will point out the key constructs which are core to the problem definitions and methods. Please note that the underlying ideas are common across multiple disciplines in Operations Research, Economics, Marketing Science, Computer Science, Machine Learning, and Artificial Intelligence.
By the end, you will have a basic knowledge of:
- What prescriptive data science is.
- Two key dimensions to define prescriptive data science problems: number of agents and dependency across time periods
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