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Data Science in Education Using R de Ryan A. Estrellado

Data Science in Education Using R

Autor Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez
en Limba Engleză Paperback – 17 feb 2027
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.
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Specificații

ISBN-13: 9781032845036
ISBN-10: 1032845031
Pagini: 280
Ilustrații: 88
Dimensiuni: 174 x 246 mm
Ediția:2
Editura: Taylor & Francis
Colecția Routledge
Locul publicării:Oxford, United Kingdom

Public țintă

Postgraduate, Professional Reference, and Undergraduate Advanced

Cuprins

Chapter 01. Introduction: Data science in education – you're invited to the party! | Chapter 02. How to use this book | Chapter 03. What does data science in education look like? | Chapter 04. Special considerations | Chapter 05. Getting started with R and RStudio | Chapter 06. Foundational skills | Chapter 07. Walkthrough 1: The education data science pipeline with online science class data | Chapter 08. Walkthrough 2: Approaching gradebook data from a data science perspective | Chapter 09. Walkthrough 3: Using school-level aggregate data to illuminate educational inequities | Chapter 10. Walkthrough 4: Longitudinal analysis with federal data on students with disabilities | Chapter 11. Walkthrough 5: Text analysis with social media data | Chapter 12. Walkthrough 6: Exploring relationships using social network analysis with social media data | Chapter 13. Walkthrough 7: Multilevel models | Chapter 14. Walkthrough 8: Predicting student pass/fail outcomes using supervised machine learning | Chapter 15. Introducing data science tools to your education job | Chapter 16. Teaching data science | Chapter 17. Learning more | Chapter 18. Additional resources | Chapter 19. Conclusion: Where to next?

Recenzii

 
“The authors have provided the definitive guide to the topic. The combination of theory and hands-on practical tutorials make this an invaluable resource for the growing fields of learning analytics and educational data science.”
 
-          Mark Warschauer, Professor of Education and Informatics at the University of California, Irvine
 
This book is a clear, compelling guide for real-world practitioners who are ready to use modern tools of data science in the education domain. The effective data analysis content would benefit almost anyone getting started with data today, but these authors' thoughtful, focused handling of the specific issues involved in working with education data sets it apart from most introductory data science books.
-          Julia Silge, Software Engineer at Posit Software, PBC
 
There are many resources for learning how to analyze education data. But what has long been missing is an inclusive and pedagogically refined resource on how to leverage modern data science principles, workflows, and tools. Data Science in Education using R fills this massive gap and more. It will be the go to resource for the next generation of data driven education professionals. And is a beautiful exposition of how to responsibly work with data from the real, messy, world.
 
-          Dustin Tingley, Deputy Vice Provost for Advances in Learning at Harvard University

Notă biografică

 
Ryan Estrellado is a writer and educator. He is the author of the book The K–12 Educator’s Data Guidebook: Reimagining Practical Data Use in Schools and a co-author of Data Science in Education Using R. Ryan tells inspiring stories about the reality of education work, ranging from overcoming a fear of data to finding a creative practice in the workplace. He has over twenty years of experience in public education.
 
Emily Freer, PhD, is a researcher who explores who people learn, grow, and navigate complex systems. She currently leads human-centered research at Froedtert ThedaCare Health, where she works to improve patient experiences and drive organizational change.
 
Joshua M. Rosenberg (PhD, Michigan State University) is Haslam Family Professor and Associate Professor of STEM (data science) education in the Department of Theory and Practice in Teacher Education at the University of Tennessee, Knoxville. He is also Co-Director of the AI, Data, and Educational Partnerships at the University of Tennessee (ADEPT) Center and coordinator of the data science education program.
 
Isabella Velásquez is part of the Developer Relations team at Posit PBC, where she works at the intersection of programming, communication, and data analysis to help people get the most out of modern data science tools.


Descriere

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? If you’re just getting started with R in an education job, this is the book you’ll want with you.