EDISON Data Science Framework: Part 3. Data Science Model Curriculum (MC-DS) 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 Model Curricula (MC-DS)
EDISON Data Science Framework (EDSF)
Data Science
Data Scientist Professional
Data Science Curriculum Design

description: The presented Data Science Model Curriculum is a part of the EDISON Data Science Framework (EDSF), providing a foundation for the Data Science profession definition. The EDSF includes the following core components: Data Science Competence Framework (CF-DS), Data Science Body of Knowledge (DS-BoK), Data Science Model Curriculum (MC-DS), Data Science Professional Profiles definition (DSPP), EDSF Use cases and application (EDSF-UCA).<br> <br> The MC-DS is built based on CF-DS and DS-BoK, where Learning Outcomes are defined based on CF-DS competences and Learning Units are mapped to Knowledge Units in DS-BoK. In its own turn, Learning Units are defined based on the ACM Classification of Computer Science (CCS2012) and reflect typical courses naming used by universities in their current programmes. The suggested Learning Units are assigned suggested labels, marking their relevance to the core Data Science knowledge areas in the form of Tier 1, Tier 2, or Elective courses. Further MC-DS refinement will be done based on consultation with the university community and experts both in Data Science and scientific or industry domains.<br> <br> The proposed MC-DS intends to provide guidance to universities and training organisations in the construction of Data Science programmes and individual courses selection that are balanced according to requirements elicited from the research and industry domains. MC-DS can be used for the assessment and improvement of existing Data Science programmes with respect to the knowledge areas and competence groups that are associated with specific professional profiles. When coupled with individual or group competence benchmarking, MC-DS can also be used for building individual training curricula and professional (self/up) skilling for effective career management.<br> <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> <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> <br> The EDSF documents are available for public discussion at the EDISON Community initiative at https://github.com/EDISONcommunity/EDSF/wiki/EDSFhome<br>