Preparing social scientists for the world of big data

This month sees the first cohort of students completing their courses and starting work placements with the Cambridge Undergraduate Quantitative Methods Centre (CUQM). Established last year in the Department of Sociology, the Centre is dedicated to improving the provision of quantitative methods training to social science and humanities undergraduates in Cambridge.

“The UK is already way ahead of many other countries in the availability of large datasets that can be used to inform both policy and social science research,” says Burchell. “Over the next few decades – the career span of current undergraduates – we are likely to see huge advances in the use of quantitative data including datasets that can only by analysed with big data techniques.”

The increasing ubiquity of big data in the social sciences stems not just from the increasing use of massive datasets in areas such as education and economics, but also to a rise in the use of ‘messier’ data – anything from the way that people engage with Twitter and Facebook, to the public records held by government agencies across Europe – which often require data ‘cleaning’ before statistical analysis can be carried out.

According to Burchell, big data is providing a huge resource that is currently underutilised, which is one of the motivations for establishing the Centre.

“We now have access to a lot of large datasets collected either at a British or a European level, but we lack people with the skills to make use of it. It’s been a bigger problem in the UK than in other countries because a lot of our school kids specialise and give up doing maths at a younger age, and there’s this idea that if you were good at numbers you’d end up doing physics or natural sciences and if you weren’t good at numbers you’d end up doing social science,” Burchell explains.

“But even if you don’t end up doing statistical analyses yourself, it’s important to understand how they’re relevant – where numbers are useful and where they can be misleading,” he adds.

Rather than increasing the basic statistical skills of all students in certain disciplines – which has been tried before in many universities – the Centre has focused on providing more advanced expertise to a proportion of undergraduates across many social science disciplines.

Some subjects, such as Psychology and Economics, already have all students graduating with good levels of quantitative skills. CUQM aims to increase the proportion of graduates leaving Cambridge with these advanced skills in the other social sciences, better preparing them to work with large datasets themelves or to understand how others draw conclusions from them.

“These skills will become increasingly vital for careers in social science research, but they will also make students much more employable in most other sectors as well,” says Burchell. The Centre also works to find placements for students with organisations like YouGov, so that they can experience how statistics skills will be relevant in the workplace.

The first year’s activities have been open to students of archaeology, biological anthropology, education, history, land economy, linguistics, politics, social anthropology and sociology. In the coming year, the Centre will extend the exposure to statistics in the social science courses at Cambridge, as well as introducing more examples of quantitative methods into the teaching of these disciplines. CUQM also aims to provide optional vacation courses to those students who currently don’t have a quantitative data analysis component to their degree, thus preparing more social scientists to engage with the world of big data.

CUQM is part of a wider initiative to train social scientists in research methods at the University of Cambridge. The Social Science Research Methods Centre, for instance, complements the work of CUQM by teaching quantitative methods to graduate students, post-docs and lecturers.

@CamQuantMethods