The government has added a new hub to its programme to boost civil servants’ data science skills as it opens the scheme for the next round of applications.

The accelerator programme is part of efforts to encourage civil servants to make better use of the data that government holds – Photo credit: PA Images

The Data Science Accelerator programme, launched in 2015, offers training in a range of data analysis techniques for people working in government in a bid to promote innovative use of data across Whitehall.

The scheme, which has so far trained 49 people from 31 public sector organisations, is now expanding, David Wilks, digital performance manager at the Government Digital Service, yesterday announced.

In addition to three existing hubs in London, Sheffield and Newport, GDS is opening a new centre in Newcastle in partnership with the Department for Work and Pensions, to create what Wilks described as a “great geographical spread”.

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The scheme is part of the government’s efforts to improve the way it uses the vast amounts of data that it holds. This includes moves to make it easier for departments to share data with each other through new rules set out in the Digital Economy Bill, which is currently making its way through parliament.

However, data analytics experts have argued that such efforts will only be successful in increasing the use of data by government if civil servants are more open to the idea and aware of the potential that the data offers them. At a recent event in Oxford, Helen Margetts, director of the Oxford Internet Institute, said that organisations often “know very little” about their data or how much data science expertise there is within the organisation.

The accelerator aims to address this, with GDS saying that, as well as offering people the chance to gain analytical skills, it hoped participants would “go on to help promote data science in government”.

The scheme involves participants working at one of the hubs for one day a week over the course of three months – they will spend the rest of their time at their home department – and travel costs will be covered by the home department.

Participants will work on a project that uses data that is already accessible to tackle a business problem, preferably for the department they work in, which they have to pitch when they apply to the accelerator programme. They will work with a mentor to develop new skills, such as machine learning, webscraping and data visualisation, on this alpha project.

Previous projects have included work by someone at HMRC to see whether publicly available data, such as from Companies House, could be used to help predict insolvency, and a project looking at how DWP could use text analysus to improve its understanding of complaints through JobCentre Plus services.

Callum Staff, a previous participant who used the Food Standards Agency’s Food Hygiene Rating Scheme to try and create predictive measures to help the agency target its interventions, said that the scheme “gives you a safe space to learn and try new techniques” in data science, as well as helping government as a whole make the best use of the data that it holds.

The role specification said that applicants must have strong analytical skills, a “big appetite for doing something ambitious” and approval of their line manager. Wilks said that coding experience is “good but not essential”.

As well as looking to recruit a new cohort of participants, GDS is also looking for new mentors for the scheme, who will provide one-to-one support to a successful applicant and gain coaching and leadership skills.

Mentors should be working in analytical roles at senior executive officer level or higher and have experience of applied statistics, intensely data-driven projects and be able to code in more than one language, ideally open-source languages like R and Python.

Potential mentors are asked to contact Wilks directly, while applicants for participants are via a form available on GOV.UK. Applications for the latest round opened yesterday and close at the end of the month.