The aims of the Dynamics of data science skills project are to:
- Identify what may be different about the data science research landscape in comparison to other disciplines, specifically the movement of talent and skills between academia, industry and the public sector, and understand the lessons that can be learned from other disciplines. The scope of this project will exclude specific focus on experimental scientific data
- Consider the interplay between dynamics of data and data science in terms of how access to data influences the movement of skills, and how data management and governance practices create need for certain data science skills such as data cleaning
- Identify lessons to be learned from industry's success in data science and how universities, smaller businesses and the public sector can best support research excellence and innovation, and access data science talent
- Investigate the mechanisms, models and policy options that would enable a stronger business-university-public sector relationship in data science, including mechanisms that would allow researchers to thrive in multiple roles across the research landscape at the same time, or move with ease between them
- Make proposals for future university-business-public sector interaction to ensure the UK can capitalise on its comparative strength in this area, use data science skills for public benefit, and ensure that learning and good practice spread as fast as possible. These will be focused on practical actions that can be taken
- Build on and support cross-Society messaging on STEM skills, research culture and research translation in order to address the issue of overall supply of talent in data skills
Who informed this project?
This project was guided by a Working Group.
The project was also informed by a series of evidence gathering events, involving a range of stakeholders across academia, government and private sector.
What came out of this project?
A project report was published in May 2019. Additionally two companion packs were published alongside this. The first companion pack presents examples of existing models and mechanisms in place around the country which could be spread more widely to address some of the areas for action identified in the report. The second companion pack documents personal stories as case studies of a range of data science career paths.