A breadth of organisations, spanning academic, industry, learned societies and professional bodies contribute to addressing the skills challenges in data science and AI, and to identify good practices for education and skills provision. The data science education interest group brings these groups together to identify and address key challenges in research and innovation in data science and AI pedagogy and its implications for policy.
The principal activities of the Data Science Education group are the following:
- To identify and work to resolve key challenges in data science pedagogy, its application, and explore its impact on the national policy landscape.
- Cohort building among researchers, educators and industry to emphasise professional connection and skill exchange, lowering barriers to collaboration.
- To provide longitudinal mentoring to support the uptake of new educational practices.
The group will encourage members to:
- share ideas, knowledge, experiences and achievements;
- foster new collaborations;
- align and expand existing education and training activities;
- reflect upon and raise awareness of specific challenges in equality, diversity and inclusion in the field; and
- pool resources and expertise to collectively unlock new funding and partnership opportunities that are not accessible to members in isolation.
How can we ensure the nation’s education and training in data science and AI continues to meet learner and workforce needs?
Lack of alignment between training offerings and the needs of the workforce is a key inhibiting factor in both education and industry. Translation of academic innovation into industry is a key barrier to AI adoption; meanwhile, taught programmes rarely expose learners to the scale of problems faced in industry and research practice, nor provide the opportunity to develop business and sector awareness. This leads to challenges not only in supporting the translation of AI into practice, but the translation of leading industry practice back into the national training curriculum. How can we bring together industry and academic communities to share best practice around data science and AI education?
How should we best support equality and diversity in our data science curricula?
Diversity is essential to developing a robust data science skills base; therefore, we need to draw on the country’s entire intellectual capital. The inequitable impact of COVID-19 on women and BAME communities is likely to have slowed progress on diversity and inclusion in all sectors. When we consider the Office for National Statistics’ estimate that over 70% of 1.5 million roles at risk of automation are held by women, it is clear that urgent intervention is required. How can we democratise and widen access to training which is responsive and “at scale” and supports the creation of a culture and environment that retains talent, and a diverse workforce?