Recognition of formal and non-formal training in data science

Ernestina Menasalvas, Nik Swoboda, Ana Moreno, Andreas Metzger, Aristide Rothweiler, Niki Pavlopoulou, Edward Curry

Research output: Chapter in Book or Conference Publication/ProceedingChapterpeer-review

3 Citations (Scopus)

Abstract

The fields of Big Data, Data Analytics and Data Science, which are key areas of current and future industrial demand, are quickly growing and evolving. Within Europe, there is a significant skills gap which needs to be addressed. A key activity is to ensure we meet future needs for skills and align the supply of educational offerings with the demands from industry and society. In this chapter, we detail one step in this direction, a programme to recognise Data Science skills. The chapter introduces the data skills challenge and the importance of formal and non-formal education. It positions data skills within a framework for skills and education, and it reviews key projects which have advanced the data skills agenda. It then introduces recognition frameworks for formal and non-formal Data Science training, and it details a methodology to achieve consensus between interested stakeholders in both academia and industry, and the platforms needed to be deployed for the proposal. Finally, we present a case study of the application of recognition frameworks within an online educational portal for students.

Original languageEnglish
Title of host publicationThe Elements of Big Data Value
Subtitle of host publicationFoundations of the Research and Innovation Ecosystem
PublisherSpringer International Publishing
Pages311-332
Number of pages22
ISBN (Electronic)9783030681760
ISBN (Print)9783030681753
DOIs
Publication statusPublished - 1 Aug 2021
Externally publishedYes

Keywords

  • Big Data
  • Data skills recognition
  • Education hub
  • Skill badges
  • Skill labels

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