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The data science frameworka view fro...

Cuadrado-Gallego, Juan J.

The data science frameworka view from the Edison Project /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The data science frameworkedited by Juan J. Cuadrado-Gallego, Yuri Demchenko.
其他題名:	a view from the Edison Project /
其他作者:	Cuadrado-Gallego, Juan J.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xiv, 194 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Data miningStudy and teaching.
電子資源:	https://doi.org/10.1007/978-3-030-51023-7
ISBN:	9783030510237$q(electronic bk.)

The data science frameworka 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$q(electronic bk.)

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

677068
Data mining
--Study and teaching.

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

Dewey Class. No.: 006.312

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