Prescriptive Data Science: Single Agent / Static Problem - Joint Price Optimization (1/2)

Welcome back. In the previous article, I have described four different types of prescriptive data science problems, which depend on two key dimensions: (1) number of agents and (2) dependency across time periods. 

In this article, I will show how to solve a prescriptive data science problem, starting with the simplest case: single agent / static problem. To make it concrete, I will use joint (regular) price optimization as a sample use case. Please note that Scipy.optimize is the main Python library that we will use to solve this problem. In addition, familiarity with concepts from Microeconomics and Operation Research — i.e. price elasticity, demand curve, profit maximization, non-linear optimization will be helpful to follow explanation below. 

By the end of this article, you will learn: 

• Key elements of problem definition for single agent / static prescriptive data science problem
• Overview of methods to solve single agent / static prescription data science problem (i.e. optimization methods)  

In the subsequent article (2/2 of this article), a toy example will be shown with Python code to demonstrate this with a sample use case of joint price optimization for a retailer.

 

Prescriptive Data Science 101: Types of Problems

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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

 

Prescriptive Data Science: Beyond Predictive Data Science

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(1) Descriptive, predictive, and prescriptive data science

(2) Required skill-sets to excel in prescriptive data science

(3) key components of prescriptive data science and common misconception

As companies become more mature with data science, predictive data science becomes more of table stakes. Prescriptive data science are gaining more importance in setting the leaders in the data science and artificial intelligence apart from other companies. In this article, I will discuss (1) different types of data science and their relevance with the organizational maturity in data science and analytics, (2) required skill-sets to excel in prescriptive data science, and (3) key components of prescriptive data science and common misconception.