Data intensive science is a major global growth area, as the volume, complexity and rate of digital data within governments and companies continues to rapidly increase. At the same time, powerful analysis techniques continue to evolve for obtaining radical insights into large datasets, including finding clusters and anomalies, as well as detecting and predicting dominant trends and correlations in such data. This data intensive science comes at a crucial time for global development. Major worldwide challenges, as encapsulated in the United Nations' Sustainable Development Goals (SDGs), require multidisciplinary solutions, many of which include data science. Moreover, the South African National Development Plan (NDP) for 2030 recognises the need to "sharpen its innovative edge and continue contributing to global scientific and technological advancement" and "shift to a more knowledge-intensive economy". We therefore propose to build a training network in data intensive science between universities in southern UK and partners in southern Africa to help address these SDGs and NDP priorities. The cornerstones of this network will be the `Data Intensive Science Centre in SEPnet' (DISCnet) and the African Institute of Mathematical Science (AIMS) South Africa. Together, we will pilot an innovative course of training and internships for the next generation of data analysts, focusing on solving SDG-related questions in South Africa and acting as a driver of the country's economy in the 21st century. Our aim with this pilot training programme is to equip and send students to solve data science problems associated with sustainable development goals (SDGs) in SA and beyond. The specific goals of the pilot programme are to: (i) Deliver an initial cohort of at least 10 highly trained African data scientists; (ii) Provide a world-class data science school to African students, leveraging existing DISCnet training material; (iii) Prime-pump a new 8-week hand-on data science training course at AIMS with contributions from DISCnet; (iv) Contribute to the sustainable development goals via 3 month strategic student internships with South African organisations and companies, focusing on economic development and welfare; (v) Understand the details of managing an extended, sustainable training network across southern Africa. This pilot leverages considerable investment from STFC, our university partners, and the Royal Society (RS). Our long-term ambition is to create a sustainable network of comparable scale to DISCnet, e.g. approximately 25 African STEM students per year receiving our specialist training. These students will become the future data science leaders in Africa.

The CDT will train the next generation of leaders in statistics and statistical machine learning, who will be able to develop widely-applicable novel methodology and theory, as well as create application-specific methods, leading to breakthroughs in real-world problems in government, medicine, industry and science. The research will focus on the development of applicable modern statistical theory and methods as well as on the underpinnings of statistical machine learning. The research will be strongly linked to applications. There is an urgent national need for graduates from this CDT. Large volumes of complicated data are now routinely collected in all sectors of society, encompassing electronic health records, massive scientific datasets, governmental data, and data collected through the advent of the digital economy. The underpinning techniques for exploiting these data come from statistics and machine learning. Exploiting such data is crucial for future UK prosperity. However, several reports from government and learned societies have identified a lack of individuals able to exploit this data. In many situations, existing methodology is insufficient. Off-the-shelf approaches may be misleading due to a lack of reproducibility or sampling biases which they do not correct. Furthermore, understanding the underlying mechanisms is often desired: scientifically valid, interpretable and reproducible results are needed to understand scientific phenomena and to justify decisions, particularly those affecting individuals. Bespoke, model-based statistical methods are needed, that may need to be blended with statistical machine learning approaches to deal with large data. Individuals that can fulfill these more sophisticated demands are doctoral level graduates in statistics who are well versed in the foundations of machine learning. Yet the UK only graduates a small number of statistics PhDs per year, and many of these graduates will not have been exposed to machine learning. The Centre will bring together Imperial and Oxford, two top statistics groups, as equal partners, offering an exceptional training environment and the direct involvement of absolute research leaders in their fields. The supervisor pool will include outstanding researchers in statistical methodology and theory as well as in statistical machine learning. We will use innovative and student-led teaching, focussing on PhD-level training. Teaching cuts across years and thus creates strong cohort cohesion not just within a year group but also between year groups. We will link theoretical advances to application areas through partner interactions as well as through a placement of students with users of statistics. The CDT has a large number of high profile partners that helped shape our application priority areas (digital economy, medicine, engineering, public health, science) and that will co-fund and co-supervise PhD students, as well as co-deliver teaching elements.