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Data analytics: the skills need in STEM


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09:30 - 18:00


The Royal Society, London, 6-9 Carlton House Terrace, London, SW1Y 5AG


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The UK must train an increasingly large number of advanced data scientists, who combine high-level mathematics and programming ability with the deep domain knowledge and soft skills needed to work in business. These individuals are not only essential for the future productivity of the UK economy but are needed to handle the large datasets created across government and other organisations which underpin new fields of cutting edge research, such as climate change and genomics.

This meeting focussed specifically on the skills gap in data analytics and visualisation in the UK, primarily looking at science, technology and engineering industries and other employers of STEM data science graduates. It aimed to highlight the various roles data scientists are employed in, which skill sets are needed, the barriers data-using companies have in employing data scientists, and how our education and in-work training systems can create enough expert data scientists to fill the future gap. 

Please contact the Industry team for more information.

This event was organised in partnership with The Royal Statistical Society.

Royal Statistical Society

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Schedule of talks

16 November




Government perspective

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10:15-10:20 Welcome

Sir John Skehel FMedSci FRS, MRC National Institute for Medical Research, UK

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10:20-11:00 Title TBC

Sir Mark Walport

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Industry perspectives

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Dr Robert Hercock, BT

11:20-12:00 The golden age of machine learning and AI

Dr Ralf Herbrich, Managing Director of Amazon Development Center Germany GmbH


In this presentation Ralf Herbrich, director of machine learning at Amazon will talk about how computing technologies and methods such as machine learning and AI are being used by Amazon to deliver a better experience for customers. He will discuss this in the context of the trends that are driving the adoption of these new technologies by business as we enter a golden age of machine learning and AI.

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12:00-12:20 Data analytics in F1 and beyond

Dr Caroline Hargrove, Technical Director, McLaren Applied Technologies


In traditional data rich industries such as Formula 1, data analytics and simulation have been core components of the engineering cycle for decades and their interface involved mostly engineers and scientists.  But as the physical and digital worlds converge, new categories of intelligent and adaptive products and processes are emerging, creating entirely new types of experiences for consumers. This leads to an increasing need for a broader breed of data scientists and other STEM-based skilled individuals that are also able to make links across increasingly diverse disciplines such as design and behavioural sciences in order to innovate the problem space as well as the solution space.  

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12:20-12:40 Data sciences and related disciplines: an industry perspective on demand, supply and current challenges


Please note this talk will now be given by Professor Nicky Best, Head, Statistical Innovation Group, GSK

This talk will outline the various roles that graduates of mathematics, statistics, programming and data sciences currently perform at GSK; the challenges we face in terms of supply and demand; the skills we seek in those we recruit; and discuss ideas regarding the future direction and utilisation of these disciplines within the pharmaceutical industry.


Lunch and networking


Academic perspectives

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Professor Guy Nason, University of Bristol and Vice-President, Academic Affairs at the RSS

14:00-14:30 The explosion of biomedical big data

Professor Gil McVean FRS, Director of the Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, The University of Oxford


Our ability to measure human biology and health is growing rapidly both in depth, from genomics to imaging to electronic health records, and scale, with several programmes in the UK alone measuring many modalities across cohorts of 100,000 and more.  The challenges associated with storing, processing, exploring and making sense of such data are huge and require the application and integration of many different skill sets.  I will attempt to identify the key problems ahead in fostering a generation of biomedical data scientists capable of getting the most, both academically and commercially, out of the amazing opportunities such data offers.

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14:30-14:50 Data science, business & the future of society

Dr Ling Ge, Fellow, Data Science Institute, Imperial College


In this talk, Dr Ge will show how innovation is being transformed through the growth of data about customers, markets, cities and government. This is creating entrepreneurial opportunities to develop new products and services, stimulating new types of start-up business and social enterprise. It is disrupting and transforming the ways in which existing businesses and governments operate, intensifying competition and giving rise to new patterns of innovation.

Data-rich innovation is predicated on making information available for analysis. Imperial College’s Data Science Institute provides examples of how this is generating new scientific breakthroughs, services and business models. The challenge is to capture, frame, analyse and interpret data to create valuable insights. Success depends upon developing the talent of data scientists: data gatherers, data modellers and data visionaries. It also requires the right conditions for entrepreneurship to flourish. 

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Breakout sessions

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Diversity in data science


This session will explore the current issues around diversity in data science, look at diversity issues from an employer’s perspective and consider how other fields have tackled this area. There will be contributions from Faye Pressly, Associate Director, Mortimer Spinks; Jackie Clayton, Global Analytics Academy Director, AIMIA; and Jenny Dyer, Head of Diversity at the Institute of Physics. 

New and emerging technologies in data science


The session will explore how technologies such as machine learning may affect the skills gap. It will look in particular at how machine learning can be used to automate data science, the likely impact of the technology on skills needs and future directions for the development of this field. There will be contributions from Dr Anat Elhalal, Lead Technologist – Data, Digital Catapult; Dr James Geddes, Research Software Engineer, The Alan Turing Institute; and Dr Christian Steinruecken, Computational and Biological Learning, University of Cambridge.

Skills gaps in industry


This session will look at particular data skills gaps faced in industry and approaches to solve them. There will be presentations from Dr George Windsor, Senior Policy Researcher, NESTA; Dr Kim Nilsson, CEO, Pivigo and Dr Martin Goodson, VP Data Science, Skimlinks, followed by time for discussion on this topic.


Industry perspectives

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16:15-16:35 Visual Analytics: Human-computer symbiosis for the big data era

Alex Healing, Chief Researcher, BT


Although the democratisation of machine learning is proving highly promising for industrial data analytics in particular, never has it been more important to ensure that human experts remain in the loop. The emerging field of Visual Analytics seeks to facilitate effective human-computer symbiosis through presenting data, and any automated processes acting upon the data in an intuitive and interactive manner, with an emphasis on sense-making and steering of underlying algorithms. Such technology may reduce the required level of skills, but rather than circumventing data scientists, it offers a promising means of bridging the gap between them and the growing pool of business analysts.

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16:35-16:55 Delivering business value with embedded corporate analytics

Adrian Mardell, Transformation Director, Jaguar Land Rover


Over the past five years, Jaguar Land Rover has doubled sales and employment, becoming the UK’s largest automotive manufacturer.  In this fast changing environment, basic analytics has often provided critical business information before IT systems can be finalised.  Now that data volumes are stretching existing tools and modern analytical tools and techniques are creating new business opportunities, we are looking to both attract and retain talent skilled in these emerging areas and also train and empower our existing workforce.  This talk will look at our ideas on embedding analytical excellence into an organisation and the processes and skills needed to turn data into insights and insights into business improvement and optimisation.

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16:55-17:00 Closing remarks

Data analytics: the skills need in STEM The Royal Society, London 6-9 Carlton House Terrace London SW1Y 5AG UK