EDISON Data Science Framework (EDSF) Extension to Address Transversal Skills Required by Emerging Industry 4.0 Transformation

Abstract

The emerging data-driven economy (also defined as Industry 4.0 or simply 4IR), encompassing industry, research and business, requires new types of specialists that are able to support all stages of the data lifecycle from data production and input, to data processing and actionable results delivery, visualisation and reporting, which can be collectively defined as the Data Science family of professions. Data Science as a research and academic discipline provides a basis for Data Analytics and ML/AI applications. The education and training of the data related professions must reflect all multi-disciplinary knowledge and competences that are required from the Data Science and handling practitioners in modern, data-driven research and the digital economy. In the modern era, with ever faster technology changes, matched by strong skills demand, the Data Science education and training programme should be customizable and deliverable in multiple forms, tailored for different categories of professional roles and profiles. Referring to other publications by the authors on building customizable and interoperable Data Science curricula for different types of learners and target application domains, this paper is focused on defining a set of transversal competences and skills that are required from modern and future Data Science professions. These include workplace and professional skills that cover critical thinking, problem solving, and creativity required to work in highly automated and dynamic environment. The proposed approach is based on the EDISON Data Science Framework (EDSF) initially developed within the EU funded Project EDISON and currently being further developed in the EU funded MATES project and also the FAIRsFAIR projects.