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# Preface {.unnumbered #sec-preface}

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Hello and welcome! This book is your companion in exploring the realm of modeling in data science. It is designed to provide you something useful whether you're a beginner looking to learn some fundamentals, or an experienced practitioner seeking a fresh perspective. Our goal is to equip you with a better understanding of how models work and how to use them, including both basic and more advanced techniques, where we touch on everything from linear regression to deep learning. We'll also show how different models relate to one another to better empower you to successfully apply them in your own data-driven projects. This book focuses on the core things you need to know to use models effectively while maintaining a focus on application. We aim to provide an overview on how to use both machine learning and traditional statistical modeling in a practical fashion, with a balanced focus on interpretability and predictive power. Join us on this exciting journey as we explore the world of models in data science!

>This is still a work in progress, with more to come and plenty of things to clean up still.  This should be out on CRC press in 2024. We welcome any feedback in the meantime as it develops, so please feel free to create an issue. For contributions, please see the [contributing](CONTRIBUTING.md) page for more information if available, other. Thanks for reading!


## What Will You Get Out of This Book?

We're hoping for a couple things for you as you read through this book. In particular, if you're starting your journey into data science, we hope you'll leave with:

- A firm understanding of modeling basics from a practical perspective
- A toolset of models and related ideas that you can instantly apply for competent modeling

If you're already familiar with modeling, we hope you'll leave with:

- Additional context for the models you already know
- Some introduction to models you don't know
- Additional understanding of how to choose the right model for the job and what to focus on

For anyone reading this book, we especially hope you get a sense of the commonalities between different models and a good sense of how they work.


## Brief Prerequisites

You'll definitely want to have some familiarity with R or Python (both are used for examples), and some very basic knowledge of statistics will be helpful. We'll try to explain things as we go, but we won't be able to cover everything. If you're looking for a good introduction to R, we recommend [R for Data Science](https://r4ds.had.co.nz/) or the [Python for Data Analysis](https://wesmckinney.com/book/) book for Python. Beyond that, we'll try to provide the context you need so that you can be comfortable trying things out. 




## About the Authors

**Michael** is a senior machine learning scientist for [Strong Analytics](https://strong.io). Prior to industry he honed his chops in academia, earning a PhD in Experimental Psychology before turning to data science full-time as a consultant. His models have been used in production across a variety of industries, and can be seen in dozens of publications across several disciplines. He has a passion for helping others learn difficult stuff, and has taught a variety of data science courses and workshops for people of all skill levels in many different contexts. 

He also maintains a [blog](https://m-clark.github.io), and has several posts and long-form documents on a variety of data science topics there. He lives in Ann Arbor Michigan with his wife and his dog, where they all enjoy long walks around the neighborhood.

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**Seth** is the Academic Co-Director of the Master of Science in Business Analytics (MSBA) and Associate Teaching Professor at the University of Notre Dame for the IT, Analytics, and Operations Department. He likewise has a PhD in Applied Experimental Psychology and has been teaching and consulting in data science for over a decade. He is an excellent instructor, and teaches several data science courses at the undergraduate and graduate level. 

Seth lives in South Bend, Indiana with his wife and three kids, and spends his free time lifting more weights than he should, playing guitar, and chopping wood.

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