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2020 | Book

Introduction to the Data Science Framework Read chapter Read first chapter

The Data Science Framework

A View from the EDISON Project

Editors: Prof. Dr. Juan J. Cuadrado-Gallego, Dr. Yuri Demchenko

Publisher: Springer International Publishing

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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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 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.

Table of Contents

Frontmatter
Chapter 1. Introduction to the Data Science Framework
Abstract
This initial chapter, “Introduction to a data science framework”, presents the main concepts related to the subject of the book. Starting with the common word about, the chapter presents an introduction to data science, the main subject of the book; to EDISON, the European Union- (EU) funded project under which most of the contents of this book were developed; and to the data science framework proposal developed in the EDISON project, and a more in-depth explanation, in the following chapters, will be the larger part of the book. The chapter will end with a last about, in this case about this book, in which the contents and structure of the book will be briefly presented.
Juan J. Cuadrado-Gallego, Yuri Demchenko
Chapter 2. Data Science Competences
Abstract
This chapter presents the definition of the Data Science Competence Framework (CF-DS). CF-DS is a cornerstone in the definition of the whole data science framework and so it was developed in the EDISON project. CF-DS provides the basis for the Data Science Body of Knowledge (DS-BoK) and Model Curriculum definitions (DS-MC) and further for the Data Science Professional Profiles definition and certification (DSPP). The proposed CF-DS incorporates many of the underpinning principles of the European Union e-Competence Framework (e-CF3.0) [8] and its further standardisation as EN16234-1:2018 [9, 10]. The CF-DS and DSPP have also adopted and intend to comply with the structure of European information and communication technologies (ICT) [11] framework on professional profiles and European Skills, Competences, Occupations and Qualifications (ESCO) [7]. Corresponding information is provided in both documents CF-DS and DSPP.
Yuri Demchenko, Juan J. Cuadrado-Gallego
Chapter 3. Data Science Body of Knowledge
Abstract
This chapter presents the definition of a consistent body of knowledge for data science (DS-BoK), which have three main objectives: (1) Support the competence groups defined in the Competences Framework for Data Science (CF-DS) presented in the previous chapter; (2) reflect the data-lifecycle management where different organisational roles, functions, competences and knowledge are required; and (3) ensure knowledge transferability and education programmes compatibility.
Juan J. Cuadrado-Gallego, Yuri Demchenko
Chapter 4. Data Science Curriculum
Abstract
This section provides background information and best practices in building effective professional curricula for specific domains of knowledge, target groups and purposes. The reviewed selected learning model and curricula design models are used to develop the EDISON approach that is targeted to provide quality education and training for specific groups of data science-related professions to acquire necessary competences and skills.
Tomasz Wiktorski, Yuri Demchenko, Juan J. Cuadrado-Gallego
Chapter 5. Data Science Professional Profiles
Abstract
This chapter treats all the aspects related to defining the Data Science Professional Profiles that can be also called data-related occupations family. The proposed occupations are placed in four top classification groups: managers, for managerial roles; professionals, for applications developers and for infrastructure engineers; technicians and associate professionals, for operators and technicians; and clerical support workers, for data curators and stewards.
Yuri Demchenko, Juan J. Cuadrado-Gallego
Chapter 6. Use Cases and Applications
Abstract
This chapter includes a set of use cases and examples of practical use of EDSF for developing data science curricula, competences assessment, data science team building and addressing new skills demand for emerging data economy.
Yuri Demchenko, Luca Comminiello, Tomasz Wiktorski, Juan J. Cuadrado-Gallego, Oleg Chertov, Ernestina Menasalvas, Ana M. Moreno, Nik Swoboda, Steve Brewer
Backmatter
Metadata
Title
The Data Science Framework
Editors
Prof. Dr. Juan J. Cuadrado-Gallego
Dr. Yuri Demchenko
Copyright Year
2020
Electronic ISBN
978-3-030-51023-7
Print ISBN
978-3-030-51022-0
DOI
https://doi.org/10.1007/978-3-030-51023-7

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