EDISON Data Science Framework: Part 2. Data Science Body of Knowledge (DS-BoK) Release 4
Creators: name: Demchenko, Yuri
name: Wiktorski, Tomasz
name: Belloum, Adam
name: Brewer, Steve
name: Cuadrado Gallego Juan José
Publisher: name: Zenodo
Year: 2022
Subjects: Data Science Body of Knowledge (DS-BoK)
EDISON Data Science Framework (EDSF)
Data Science
Data Scientist Professional
Data Science Curriculum Design

description: The Data Science Body of Knowledge defines in a structured way the knowledge topics required for Data Science professionals to work efficiently with their work tasks and further develop their professional competences and skills. The definition of the Data Science Body of Knowledge provides a basis for defining the Data Science Model Curriculum and further can be used for the Data Science professional certification.<br> The presented DS-BoK defines six groups of Knowledge Areas (KAG) that are linked to the identified competence groups defined in CF-DS: KAG-DSA Data Analytics; KAG-DSDM Data Management, KAG-DSENG Data Science Engineering, KAG-DSRMPM Research Methods and Project Management; and KAG-DSBPM Business Process Management as an addressed domain knowledge area. Knowledge Areas Groups include few Knowledge Areas. Knowledge Areas (KA) are defined in most cases by existing scientific subjects according to ACM Computing Classification System (ACM CCS2012) or commonly used academic subjects. New KAs are introduced based on existing and relevant Data Science technology areas. Knowledge Areas are composed of a number of Knowledge Units (KU) which are currently the lowest component of the DS-BoK. Defining the domain knowledge groups both for science and business will be a subject for further DS-BoK development in tight cooperation with domain specialists.<br> The proposed EDSF and DS-BoK in particular, are intended to provide guidance and a basis for universities and education practitioners to define their Data Science curricula and select necessary courses, on the one hand, and for companies to better define a set of required competences, knowledge and skills for their specific industry domain in their search for Data Science talents, on the other hand.<br> The EDISON Data Science Framework (EDSF) includes the following main components: Data Science Competence Framework (CF-DS), Data Science Body of Knowledge (DS-BoK), Data Science Model Curriculum (MC-DS), Data Science Professional Profiles (DSPP), which are extended with new Part 5. EDSF Use cases and applications (EDSF-UCA). The EDSF provides a conceptual basis for the Data Science Profession definition, targeted education and training, professional certification, organizational capacity building, and organisation and individual skills management and career transferability.<br> The initial definition of the EDISON Data Science Framework (EDSF) was done in the Horizon2020 Project EDISON (Grant 675419) that produced Release 1 in 2016 and published Release 2 in 2017. Currently, EDSF is maintained by the EDISON Community initiative that is coordinated by the University of Amsterdam. The new EDSF Release 4 is the product of the wide community of academicians, researchers and practitioners that are practically involved in Data Science and Data Analytics education and training, competences and skills management in organisations, and standardisation in the area of competences, skills, occupations and digital technologies. In particular, the current release incorporates revisions to competences proposed during the Data Stewardship Professional Competence Framework (CF-DSP) definition by the FAIRsFAIR project (Grant 831558).<br> The EDSF documents are available for public discussion at the EDISON Community initiative at https://github.com/EDISONcommunity/EDSF/wiki/EDSFhome