Designing and Delivering a Curriculum for Data Science Education Across Europe

Abstract

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A cross-institutional analysis of data-related curricula in information science programmes: A focused look at the iSchools

“…The questions guiding this study are important as information science programmes consider how to best respond to trends and educational needs and prepare the next generation of information professionals. The research questions pursued are reflected in recent initiatives, such as the European Commission–supported EDISON Data Science Framework (EDSF) [39] and the European Data Science Academy (EDSA) [40], as well as the US National Science Foundation’s commissioned reports, such as, Realising the Potential of Data Science: Final Report from the National Science Foundation Computer and Information Science and Engineering Advisory Committee Data Science Working Group [41,42] and Envisioning the Data Science Discipline: The Undergraduate Perspective: Interim [43]. An in-depth comparison of these initiatives is beyond the scope of this immediate article, although a high-level review reveals curricula recommendations similar to current iSchool activity in the data space.…”

Content Analysis of Two-year and Four-year Data Science Programs in the United States

“…In a similar study [13], researchers monitored trends across Europe in order to assess the demands for particular Data Science skills and expertise. They [13] used automated tools for the extraction of Data Science job posts as well as interviews with Data Science practitioners.…”

“…In a similar study [13], researchers monitored trends across Europe in order to assess the demands for particular Data Science skills and expertise. They [13] used automated tools for the extraction of Data Science job posts as well as interviews with Data Science practitioners. The goal of the study [13] was to find the best practices for designing Data Science curriculum which include; industry aligned, use of industry standard tools, use of real data, transferable skill set, and concise learning goals.…”

“…They [13] used automated tools for the extraction of Data Science job posts as well as interviews with Data Science practitioners. The goal of the study [13] was to find the best practices for designing Data Science curriculum which include; industry aligned, use of industry standard tools, use of real data, transferable skill set, and concise learning goals. The best practices for delivery of Data Science Curriculum include multimodality, multi-platform, reusable, cutting-edge quality, reflective and quantified, and hands-on.…”

“…The best practices for delivery of Data Science Curriculum include multimodality, multi-platform, reusable, cutting-edge quality, reflective and quantified, and hands-on. In addition, the researchers [13] indicated that the curriculum design should be from user, research, industry and professional recommendations and feedback. The curriculum should be flexible and adaptable, and should address the whole Data Value Chain.…”