Contact for Details
3 years
London
Tuition Fee
£9,790 per year (UK home)
Full-time, Part-time
The tuition fee for UK home students is £9,790 per year for full-time study. Tuition fees for subsequent years may be subject to inflationary increases by the UK government. The university reserves the right to increase the tuition fees in line with changes to legislation, regulation and any government guidance or decisions. More information on the latest fee can be found on the institution website.
Learn about our mission to make higher education accessible and empowering for every
A minimum grade C in three A levels (or a minimum of 96 UCAS points from an equivalent Level 3 qualification, eg BTEC Level 3 Extended Diploma, Advanced Diploma, Progression Diploma or Access to Higher Education Diploma of 60 Credits). English language and Mathematics GCSEs at grade C/4 or above (or equivalent).
GCSE: Grade C/4 or above (or equivalent).
Graduates are prepared for roles such as data analyst, data scientist, data engineer, and business intelligence developer, with opportunities in sectors ranging from technology to finance.
Assessment methods include a combination of coursework, in-class tests, and exams, often involving the analysis of datasets, application of algorithms, and written reports.
The institution provides support for career development, including modules focused on professional skills and opportunities to engage with industry-related activities.
Students are encouraged to undertake professional activities, including work-based placements, research-related projects, and volunteering, to gain practical experience.
The course is developed with input from industry experts and focuses on a blend of mathematics and applied computing, providing hands-on experience with modern data tools.
Yes, the course emphasises practical skills through workshops, tutorials, and the use of industry-standard software like Python, SQL, and Hadoop.
The learning experience includes lectures, tutorials, and workshops, supported by blended learning technologies like WebLearn and access to extensive digital resources.
Prospective students should be prepared for a rigorous curriculum that combines mathematical theory with practical programming and data analysis techniques.
