A national programme in the UK - Q-Step - set out in 2013 to develop a future cohort of quantitatively trained social science students. This ten-year step-change initiative is now two-and-a-half years old. Fifteen universities won funding from the Nuffield Foundation, the Higher Education Academy for England and the Economic and Social Research Council to experiment and innovate with teaching quantitative methods in order to address the issue of this ‘quantitative deficit’ (Nuffield, 2013). The national drivers for this programme are in part the British Academy’s Count Us In: Quantitative Skills for a New Generation’ report from which the title for this paper was taken.

We will present the approach we are taking at the University of Manchester, through our Q-Step Centre, and how this is making a difference to students on politics, sociology, criminology, philosophy and linguistics courses. Most of the students have not taken maths or statistics beyond the age of sixteen. Our method is built on two pillars. First, we give students pedagogically sound teaching experiences in the classroom, backed up with computer lab sessions where they practice their statistical skills (Buckley et al, 2015; Brown, 2013). The students confront data downloaded from large, nationally representative survey, such as the British Social Attitudes survey or the National Crime Survey for England; often we make the students part of the dataset, to introduce statistical concepts. We design our courses to make numbers a normal part of the social science curriculum, introducing quantitative data and methods from the first year. Second, we have introduced a paid internship programme for second year students where a sub-set have opportunity to undertake data-driven, research-led projects for up to 8 weeks in external organisations. To date seventy students have been placed, across two summers, with up to sixty more due to undertake internships in 2016. They are hosted in public, private and voluntary bodies where they spend the summer doing applied data analysis.

Our work shows that even with basic statistical teaching from undergraduate courses students are well-placed to undertake real-world data analysis in the workplace. The paper will draw together main themes, from the seventy students who have participated, to illustrate the types of analysis undergraduates can undertake, with appropriate support both academically and in the host organisation. Cases will be used to show the capacity for developing quantitative research methods through experiential learning. Examples will also be shared showing what happens after the internships, regarding job offers, postgraduate research training opportunities and employability prospects.

We also challenge the ‘quantitative skills (QS) deficit’ and ‘skills gap’ discourses as our experience shows that many of our students excel at creative thinking, teamwork and writing. The opportunities afforded to them through a quantitative data research internship result in far more than just developing their QS. Indeed this work provides an opportunity to contribute further to the literature on why social scientists are highly employable graduates (AfSS, 2013)

Keywords: Statistics education research; Statistical literacy; Internships; Applied learning