Editors:
- Consolidates results of EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers)
- Explains EDSF (EDISON Data Science Framework)
- Can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams
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Table of contents (6 chapters)
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Front Matter
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Back Matter
About this book
This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader.
The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models.
The book can be used to developnew courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines.
Editors and Affiliations
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Department of Computer Science, University of Alcalá, Madrid, Spain
Juan J. Cuadrado-Gallego
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Faculty of Science, Universiteit van Amsterdam, Amsterdam, The Netherlands
Yuri Demchenko
Bibliographic Information
Book Title: The Data Science Framework
Book Subtitle: A View from the EDISON Project
Editors: Juan J. Cuadrado-Gallego, Yuri Demchenko
DOI: https://doi.org/10.1007/978-3-030-51023-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-51022-0Published: 02 October 2020
Softcover ISBN: 978-3-030-51025-1Published: 03 October 2021
eBook ISBN: 978-3-030-51023-7Published: 01 October 2020
Edition Number: 1
Number of Pages: XIV, 194
Number of Illustrations: 4 b/w illustrations, 31 illustrations in colour
Topics: Data Structures and Information Theory, The Computing Profession, Data Engineering, Statistics, general