01-intro.Rmd

# Introduction 

Dear Data Scientists, Educators, and Data Scientists who are Educators: 

This book is a warm welcome and an invitation. If you're a data scientist in education or an educator in data science, we know that your role isn't exactly straightforward. We welcome everyone who wants to understand data science in education better. 

If you work in education or data science, you also own a part of the solution. We invite everyone to help define what it means to practice data science in education by sharing their experiences. 

### The Challenge of Data Science in Education 

We'll get to work on understanding data science in education soon, but first let's talk about why this relationship is not such a straightforward thing. 

Talking about data science in education is hard because everyone tackles it on different levels. If education were a building, it would be multi-storied with many rooms. There are privately and publicly funded schools. There are more than eighteen possible grade levels. You can be educated alone in front of a computer or with others in a classroom. 

This imaginary building also has rooms the residents never see: Business and finance staff plan for efficient use of limited funds. The transportation department plans bus routes across vast spaces. University administrators search for the best way to measure career readiness. 

So why don't we see more data science happening in these areas of education? Data science is a relatively new field. This means that our community is still trying to work out how it all fits in. It also means that folks in education aren't always used to having someone around [who understands education, knows how to code, and can use statistical techniques](http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) all at once. 

### Meeting the Challenge 

As the data science field grows, we'll need better language to describe what it means in education and how to use it to meet our goals for students. In this book we want to take a step towards understanding data science in education better by exploring challenges you're likely to encounter no matter how you work with data in education. After that we describe basic and advanced data science skills that you can use to tackle these challenges. Finally, we'll present walkthroughs of analyses conducted in the education setting to bring these challenges and techniques to life. 

We hope after reading this book you'll feel like you're not alone in defining how to do data science in your education job. We also hope the techniques and examples here give you ideas to kickstart using data science to meet your goals in education. Finally, we hope you accept our invitation to contribute to this work by sharing your own challenges and solutions.