Data Science in Education Using R
I’m super excited to share that I will be writing a book, Data Science in Education Using R. The book will be published by Routledge sometime in mid-to-late 2020, and, importantly to us, Routledge agreed to let us keep the copyright for a web-version of the book, meaning that we will publish the entire contents of the book in an open and freely-available format (along with the print and e-book versions, which will be available for sale).
The book will be published with an amazing group of co-authors: Emily Bovee, Ryan Estrellado, Jesse Motstipak, me, and Isabella Velásquez. While the aims of the book may be ambitious (trying to speak to the role of data science across education roles), I know we have the right people (I’ll leave it to you to decide about this co-author) to do it.
The project has been a bit of a labor of love and so it is very rewarding to be able to (finally) announce that the book will be published.
The book will be published with an amazing group of co-authors: Emily Bovee, Ryan Estrellado, Jesse Motsipak, me, and Isabella Velásquez. While the aims of the book (trying to speak to the role of data science across education roles) may be ambitious, I know we have the right people (I’ll leave it to you to decide about this co-author) to do it.
Here’s a short description from the proposal:
This book aims to give data science in education a common language and set of skills so that the community around this topic can grow and learn more together. It is a friendly welcome to educators who are interested in exploring techniques for data analysis in education. It adds to the growing body of data science books by demonstrating data science using the language, datasets, problems, and workflows that are specific to education. It aims to help educators move from “knowing about” data analysis tools to “knowing how to” conduct meaningful data analyses in their work. This book is ideal for educators at all grade levels looking to start exploring how to use data science in their everyday work, educators who are looking to level up their use of data by learning about programming and machine learning, data-savvy consultants who help educators meet their goals, or experienced data scientists looking for hands on examples that are directly from the education field.
Some Thanks
We started writing this book in the open, on GitHub (see the link at the bottom), and, as a result, this work was supported by many individuals it the Data Edu Slack channel in which this book took form. On behalf of my co-authors, thank you to everyone who contributed code, suggested changes, asked questions and filed issues, and even designed a logo for us: Abi Aryan, Jason Becker, William Bork, Erin Grande, Jake Kaupp, Ludmila Janda, Kris Stevens, David Ranzolin, and Bret Staudt Willet.
Thank you, too, to Enilda Romero-Hall and Mark Warschauer for sharing valuable advice on the book publishing process. Thanks to Hannah Shakespeare, our editor at Routledge, for supporting this book.
Follow the Book’s Development
A repository for the book (which will soon include a link to the in-development book) is available here on GitHub; we’ll update this throughout the writing of the book. You can also reach out to any of the authors if you have questions or wish to provide feedback about it.