With the release of Data Science in Education Using R right around the corner, Emily, Isabella, Jesse, Josh and I have been sharing what readers can expect from the book.
I’m excited to be working with Routledge again for my next book, The K-12 Educator’s Data Guidebook: Reimagining Practical Data Use in Schools. This is a project I’ve been wanting to do for a while now.
Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open source statistical programming language? And what does a data analysis project in education look like?
If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job.
This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
I wrote a book with some really awesome people. Data Science in Education Using R is an open source book about learning R and data science in the education field. The print version is now available for pre-order and will be available from Routledge in October 2020.
At the South County SELPA, I work on a project called Equity, Disproportionality & Design (ED&D). ED&D is part of the California Statewide System of Support. Our mission is to partner with SELPAs in California to prevent disproportionality in our schools.
I and my teammate Marcus Jackson got to hang out with Stevens Dormezil and Michael Ricci to talk about equity in schools, reimagining how we talk about special education data, and schoolwide intervention systems.
This is part three of a three part series where I work with California School Dashboard data by cleaning, visualizaing, and exploring through modeling. You can read the first part of this series, which shows one way to clean and prepare the data, and the second part of the series, which shows a way to visualize the data .
This is part one of a three part series where I’ll be working with California School Dashboard data by cleaning, visualizaing, and exploring through modeling.
Introduction: It’s Ok to Skip Around I’m writing this series for data scientists, public school educators, and data scientists who are also public school educators.
Note: I include a lot of code in this post so my fellow data scientists can either learn from it or give me feedback about how to make it better.
Using public school data can help school districts see the bigger picture of what is going on. So why don’t we see more districts taking advantage of this information gold mine?