Course summary
Reasons to choose Kingston
- This course aligns with a thriving area of applied machine learning research, giving opportunities for exposure to plenty of cutting-edge examples and exercises.
- This course has flexible entry points and has been designed for a variety of disciplines and backgrounds.
- Like many MSc courses in the School of Computer Science and Mathematics, Data Science benefits from a diverse community of learners. You will study in week-long blocks that can fit around different work/study patterns.
- Machine Learning and Artificial Intelligence
- Applied Data Programming
- Data Analytics and Visualisation
Modules
Example modules: Machine Learning and Artificial Intelligence, Applied Data Programming; Data Analytics and Visualisation. For a full list of modules please visit the Kingston University course webpage.
Assessment method
Assessment typically comprises in-class tests, practical (e.g. presentations, demonstrations) and coursework (e.g. essays, reports, self-assessment, portfolios, dissertation). The approximate percentage for how you will be assessed on this course is as follows: 94% coursework 3% exams and tests 3% practical (repeat for each year, if part time)
Entry requirements
A 2.1 or above honours degree in a subject with relevant computing science and mathematics/statistics content. Typical appropriate first degree subjects would include: computer science (including software engineering or cyber security), mathematics, statistics, and some engineering courses.
Fees and funding
Tuition fees
England | £9860 | Year 1 |
Northern Ireland | £9860 | Year 1 |
Scotland | £9860 | Year 1 |
Wales | £9860 | Year 1 |
EU | £17500 | Year 1 |
International | £17500 | Year 1 |
Additional fee information
Provider information
Kingston University
River House
53-57 High Street
Kingston upon Thames
KT1 1LQ