The MSc in Data Science is aimed at providing opportunities for students who wish to establish expertise and employment in data-centric, largely quantitative areas within a broad range of professional disciplines and areas of employment. A cross-disciplinary approach is therefore central to the delivery of the programme.

According to Hal Varian, Google Chief Economist, “The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it - that’s going to be a hugely important skill in the next decades.” It is these skills and the knowledge that underpins them that are the focus of this programme.

This programme includes four taught modules and a Research Dissertation, and is available in full-time and part-time modes. Delivery of taught modules is by block and blended learning.

Each taught module is based on one week's intensive attendance at the UEL Docklands campus, according to an advertised calendar, usually at the beginning of each semester. Students are expected to have a laptop computer for in-class practical sessions. During the remainder of the semester, students can work on their reading, practical components (from a workbook) and coursework. Students will be supported on campus or online by tutorials. The taught modules on this programme are available to be taken as credit bearing short courses by suitably qualified individuals.

This course gives you the opportunity to look at data across a wide range of subjects and sources, including finance, crime, the environment, housing, education, demographics and social media.

You will gain hands-on experience of handling data through your course work, projects and analysis. Recent students have been analysing crime data, drawing on material similar to that used for research undertaken by our course leaders for the Metropolitan Police and Essex Police.

They have also conducted data projects for companies such as KPMG and Thames Water as well as for the Department of Department of Health and NHS Foundation Trusts.

Course modules include Data Ecology, Quantitative Data Analysis, Spatial Data Analysis and Advanced Decision Making, as well as your research dissertation.

The specialisms of our academic team include data cleansing, data integration, data mining, spatial analysis and predictive analytics and their research engages them in a variety of data from crime statistics to natural hazards and from public health to business. It keeps them at the forefront of new developments in the field.

The cross-disciplinary approach to the subject at the University of East London means you can follow your own area of interest, enhancing your knowledge and skills under the expert guidance of your researchers.

“Our definition of Data Science is the science, engineering and practice of extracting value from data that impacts business, governance and society,” says Course Leader, Emeritus Professor Allan Brimicombe.

“We strive to maximise the potential of all our students so they can make valuable to contributions to, and enhance their careers in this new fast-growing and vibrant sector.”

The advantage of studying this programme is that it will uniquely qualify students in a field that is increasingly recognised as being central to most professional areas and for which job opportunities have been rising exponentially. Holders of an MSc in Data Science will have an advanced qualification in this area and it will prepare them for a professional or research career. Holders of this qualification will be eligible to apply for membership of the Royal Statistical Society.

Our students are professionals and graduates from a diverse range of disciplines. All are improving their career options and general expertise in this expanding market. They include data analysts from local councils, an IT teacher, an accountant, a chief software architect from Bermuda, a business manager with EDF Energy, a systems analyst, a system design analyst with Microsoft, a psychologist. Other students have graduated from IT, sports science, neuroscience, microbiology, mathematics, physics, business, economics, law, civil engineering and international management.