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Our program employs a dynamic blended learning approach, combining face-to-face instruction with various online resources to provide a comprehensive learning experience. Each module includes a minimum of three hours of weekly face-to-face contact, excluding the MSc Project module. Lectures, which cover content, methodologies, techniques, and associated issues, are delivered both in person and asynchronously online. Tutor-supported seminars and tutorials provide flexible classroom time for hands-on learning, discussion, and problem-solving scenarios. Independent study is facilitated through a wealth of resources available via the Virtual Learning Environment, including DataCamp and AWS Educate/Academy, as well as library texts, journal articles, and electronic sources.
All modules are assessed entirely through coursework-based activities, which include reports, demonstrations, presentations, and coding tasks.

Your main cost will be tuition fees. These vary depending on what you're studying. You can find the cost of tuition for your chosen subject by visiting the course page. We don't charge an application fee for our postgraduate courses.

Your tuition fees cover:

• The cost of your postgraduate degree at the University, as well as charges for registration, tuition, supervision, and examinations
• Your graduation ceremony
• The facilities and equipment you need to complete your studies, such as computer rooms, access to laptops, the Library, and laboratories
• Access to resources, including electronic journals, an alternative guide to funding, and thousands of hours of educational videos on LinkedIn Learning
• University support services, including academic, financial, careers, and wellbeing support, and personal tutors
• Membership of the Students' Union (giving you the right to vote in elections, join clubs and societies, and get free independent advice)

Stream One (First half of the programme)

• Applied Statistics for Data Science
• Data Mining & Statistical Forecasting
• Project Management & Research Methodology

Stream Two (Second half)

• Principles of Computing
• Machine Learning & Decision Making
• Big Data Engineering & Applications

The individual project runs in parallel with these modules (typically June–September for full-time students) and is often developed in collaboration with an industry partner (e.g., NHS Wales, EY, the Met Office)

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