Simulation Models for Data Science

Chapter 2. Building a Simulation Mindset: From Static Analysis to Dynamic Modeling

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This will be the 2nd chapter of the final book. Please note that the GitHub repo will be made active later on.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at [e-mail].

This book is motivated by a single conjecture: data scientists need a variety of tools that enable both retrospective analytics and forward-looking simulation. You might justifibly ask, why? Afterall data science has been around as a named discipline for at least 25 years and it’s roots go back even further into the history of statistics, computational thinking, machine learning, and database systems. Those foundational disciplines also contribute to simulation modeling so it is best to think of simulation modeling as an aspect of data science, along with statitics and machine learning. Simulation modeling is the set of practices and methods that help us address questions that traditional analytics with its retrspecitve approach are not well suited for.

In this chapter, we’ll start with a review of the main challenges facing data scientists that are difficult to analyze with traditional analytics, such as descriptive ...