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The data science framework = a view ...

Cuadrado-Gallego, Juan J.

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The data science framework = a view from the Edison Project /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The data science framework/ edited by Juan J. Cuadrado-Gallego, Yuri Demchenko.
Reminder of title:	a view from the Edison Project /
other author:	Cuadrado-Gallego, Juan J.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xiv, 194 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction to the Data Science Framework -- Data Science Competences -- Data Science Body of Knowledge -- Data Science Curriculum -- Data Science Professional Profiles -- Use Cases and Applications -- App. A, Data Science Related Process Models.
Contained By:	Springer Nature eBook
Subject:	Data mining - Study and teaching. -
Online resource:	https://doi.org/10.1007/978-3-030-51023-7
ISBN:	9783030510237

The data science framework = a view from the Edison Project /
The data science frameworka view from the Edison Project /[electronic resource] :edited by Juan J. Cuadrado-Gallego, Yuri Demchenko. - Cham :Springer International Publishing :2020. - xiv, 194 p. :ill., digital ;24 cm.

Introduction to the Data Science Framework -- Data Science Competences -- Data Science Body of Knowledge -- Data Science Curriculum -- Data Science Professional Profiles -- Use Cases and Applications -- App. A, Data Science Related Process Models.

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 develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines.

ISBN: 9783030510237

Standard No.: 10.1007/978-3-030-51023-7doiSubjects--Topical Terms:

2020245
Data mining
--Study and teaching.

LC Class. No.: QA76.9.D343 / D38 2020

Dewey Class. No.: 006.312

The data science framework = a view from the Edison Project /
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505 0 $a Introduction to the Data Science Framework -- Data Science Competences -- Data Science Body of Knowledge -- Data Science Curriculum -- Data Science Professional Profiles -- Use Cases and Applications -- App. A, Data Science Related Process Models.
520 $a 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 develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines.
650 0 $a Data mining $x Study and teaching. $3 2020245
650 0 $a Big data $x Study and teaching. $3 3503681
650 1 4 $a Data Structures and Information Theory. $3 3382368
650 2 4 $a The Computing Profession. $3 895946
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650 2 4 $a Statistics, general. $3 896933
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