Preparing tomorrow’s data scientists today
Data science is changing working practices in nearly every field and sector. As such, the skills and competences required by employers are also changing and evolving. The EU-funded EDISON project offers a consistent approach for educators, trainers, recruiters and policy makers seeking to close the gap between the skills that employers need and what the market offers. ‘The emergence of data science technologies is having an impact on nearly every aspect of how research is conducted, how scientists think, and how research data are used and shared,’ says Project Coordinator and Lead Investigator Yuri Demchenko. ‘The EDISON project put into place foundational mechanisms that will significantly increase the number of competent and qualified data scientists across Europe and beyond.’ To accomplish this, the project coordinated various measures aimed at reducing the gap between the supply side of educators and trainers and the demand side of employers. ‘Our mission was to reach out to diverse research domains and understand the needs of their researchers and their associated research infrastructures,’ adds Project Engagement Coordinator Steve Brewer. ‘We also dived into existing data science and data analytics teaching programmes to better understand what was available to prospective data scientists looking to build their skills and competences.’ The EDISON Data Science Framework The result of these efforts is the EDISON Data Science Framework (EDSF). The EDSF consists of the Data Science Competence Framework, a body of knowledge, a model curriculum and, overarching all of these, a collection of professional profiles. As a toolkit, the EDSF 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 a range of scientific disciplines. ‘Far from being prescriptive, this collection of well-researched, tried and tested documents was designed to be used as a flexible and powerful toolkit,’ says Demchenko. ‘As the framework materialised, we met an increasing number of researchers and organisations across diverse scientific domains who recognised the need for more and more people with such skills,’ adds Brewer. ‘As a consequence of this scale and complexity, the architecture of the framework expanded to accommodate the diverse roles needed to execute data-intensive science at scale.’ All EDSF documents are freely available via the project’s website as an Open Source product under Creative Common Attribution license. Tomorrow’s data scientists Even after the project’s close, work on the EDSF progresses. For example, it continues to undergo community-led development to define common ontologies and an application programming interface (API) that others can build on. This will open new possibilities for developing commercial products for data science competences and skills benchmarking, job profile and vacancies design, knowledge assessment and team building. One of the project’s key legacies is its contribution to addressing the increasing demand for data-related skills by creating a solid foundation for a new profession of data scientists and related professional profiles. ‘Our researchers continue to build on the interest generated and the momentum gathered over the course of the project,’ says Demchenko. ‘As we enter the next phase of this important initiative, we will focus on some key activities that will solidify EDISON’s groundwork and help a much broader community of data scientists support tomorrow’s data-intensive needs.’
Keywords
EDISON, data science, data scientists, data, data technology
