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E-raamat: Data Science in Education Using R [Taylor & Francis e-raamat]

  • Formaat: 284 pages, 8 Tables, black and white; 43 Halftones, black and white; 43 Illustrations, black and white
  • Ilmumisaeg: 27-Oct-2020
  • Kirjastus: Routledge
  • ISBN-13: 9780367822842
  • Taylor & Francis e-raamat
  • Hind: 161,57 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 230,81 €
  • Säästad 30%
  • Formaat: 284 pages, 8 Tables, black and white; 43 Halftones, black and white; 43 Illustrations, black and white
  • Ilmumisaeg: 27-Oct-2020
  • Kirjastus: Routledge
  • ISBN-13: 9780367822842
"This book provides educational analysts and educators with practical tools to start working in data science and data analysis - using the data usefully that is generated by school districts, government agencies, and education businesses. The authors demonstrate the principles of analysis working with R and walkthrough various scenarios and projects to show the outcome of the analysis. The book will teach education professionals to apply programming and statistics to prepare data, transform it, visualizeit, and analyze it to answer questions that are pertinent to their daily roles"--

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.

Acknowledgments ix
Preface xi
1 Introduction: Data Science In Education--You're Invited To The Party!
1(4)
2 How To Use This Book
5(8)
3 What Does Data Science In Education Look Like?
13(8)
4 Special Considerations
21(6)
5 Getting Started With R And Rstudio
27(12)
6 Foundational Skills
39(22)
7 Walkthrough 1: The Education Data Science Pipeline With Online Science Class Data
61(32)
8 Walkthrough 2: Approaching Gradebook Data From A Data Science Perspective
93(16)
9 Walkthrough 3: Using School-Level Aggregate Data To Illuminate Educational Inequities
109(18)
10 Walkthrough 4: Longitudinal Analysis With Federal Students With Disabilities Data
127(36)
11 Walkthrough 5: Text Analysis With Social Media Data
163(16)
12 Walkthrough 6: Exploring Relationships Using Social Network Analysis With Social Media Data
179(12)
13 Walkthrough 7: The Role (And Usefulness) Of Multilevel Models
191(14)
14 Walkthrough 8: Predicting Students' Final Grades Using Machine Learning Methods With Online Course Data
205(24)
15 Introducing Data Science Tools To Your Education Job
229(12)
16 Teaching Data Science
241(10)
17 Learning More
251(6)
18 Additional Resources
257(6)
19 Conclusion: Where To Next?
263(6)
Appendices 269(12)
References 281(8)
Index 289
Ryan A. Estrellado has served public schools for over seventeen years as a school psychologist and administrator. Ryan writes about working with data, education and other projects at https://ryanestrellado.com.

Emily A. Freer is the Director of Educational Development and Assessment at the Marquette University School of Dentistry. Learn more about Emily at https://emilyfreer.com.

Jesse Mostipak is the Community Advocate for Kaggle, and has worked both as a high school science teacher and lead data scientist within education non-profits. To follow Jesses learning adventures in Python and deep learning, head over to https://jessemaegan.com.

Joshua M. Rosenberg is an Assistant Professor of STEM Education at the University of Tennessee, Knoxville. Follow Joshs work at http://joshuamrosenberg.com.

Isabella C. Velásquez is a data analyst committed to nonprofit work with the goal of reducing racial and socioeconomic inequities. To follow Isabellas personal projects, join her at https://ivelasq.rbind.io.