Trains students to collect, model and analyse data using statistical and computational methods. Suited to graduates from numerate, scientific or computing backgrounds; a two-year route is also listed (year 2 fee £5,870).
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Check your fitMatch your level, budget, intake and preferences.
Use these as an early planning guide. We will confirm the exact university rules for your qualification and intake before you apply.
01Academic route
Your qualification fit
Four-year Bachelor Special or Honours degree with CGPA 2.8/4 or 55%+; or three-year Bachelor General degree with CGPA 3.3/4 or 80%+; or a UK/Ireland/Australia-affiliated degree completed within the last five years
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Scholarship information
Potential funding at Liverpool John Moores University
Automatic international scholarships of £3,000 to £4,500 off tuition fees (course pages state up to £4,000) for eligible applicants; courses with fees under £17,250 are not eligible.
Eligibility, value, availability and deadlines are decided by the university. Confirm the award for this exact course and intake before relying on it.
Programme overview
A clearer look at MSc Data Science
Trains students to collect, model and analyse data using statistical and computational methods. Suited to graduates from numerate, scientific or computing backgrounds; a two-year route is also listed (year 2 fee £5,870).
This 12-month postgraduate route concentrates on data preparation, exploration and visualisation, statistical modelling and machine-learning methods, turning complex evidence into useful decisions. The course record does not promise a placement, so students should judge it primarily by its curriculum, teaching approach and project opportunities.
Course focus
What you can expect to explore
Data preparation, exploration and visualisation
Statistical modelling and machine-learning methods
Turning complex evidence into useful decisions
Programming and computational problem-solving
Skills development
Capabilities you can build
Python or analytical programming
Statistical reasoning
Data visualisation
Model evaluation and communication
The balance of technical, academic and professional skills depends on the modules and assessments offered for your intake.
Learning experience
How you may learn
Applied work such as case studies, workshops, projects or problem-based tasks.
Independent postgraduate study, critical reading and a dissertation or major project where required.
Assessment may combine coursework, reports, presentations, examinations or projects; the exact balance is set by the university.
Who is this course for?
A strong fit for students who enjoy combining programming, statistics and problem-solving to extract meaning from complex data. Entry requirements still determine eligibility, and students should compare the official module list with their own career goals before applying.
Career direction
Where it may lead
Relevant career directions may include:
Data scientist
Data analyst
Business intelligence analyst
Machine-learning or analytics specialist
These are examples, not guaranteed outcomes. Employment depends on your experience, location, professional registration where applicable and the modules completed.
Course contents
Published course content for this programme.
8 curriculum items were captured from the university’s official course page. These may include modules, course units or published curriculum topics, and can change by intake, pathway and availability.
01
Introduction to Data Analytics20 credits
02
Statistical Methods in R20 credits
03
Big Data Computing20 credits
04
Research Methods in Data Science20 credits
05
Machine Learning and Data Mining20 credits
06
Efficient algorithms for complex data sets20 credits
07
Data Science Project60 credits
08
Work Based Project60 credits
Entry requirements
Can I apply?
Academic
Minimum 2:2 in a numerate, scientific or computer-based subject, plus A level Mathematics grade C or equivalent
Sri Lankan qualifications
Four-year Bachelor Special or Honours degree with CGPA 2.8/4 or 55%+; or three-year Bachelor General degree with CGPA 3.3/4 or 80%+; or a UK/Ireland/Australia-affiliated degree completed within the last five years
English (IELTS)
6.5 overall, minimum 6.0 in each component
English alternatives
Sri Lankan O-Level English grade B or above accepted for programmes requiring IELTS 6.5, grade C or above for programmes requiring IELTS 6.0 (no expiry date)
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More Data Science & AI courses at Liverpool John Moores University
Course information checked October 2026 from the official course page on ljmu.ac.uk. The expanded description is original GEC guidance built from the recorded course summary, award level and subject; it does not reproduce a university or partner listing word for word. Exact modules, teaching, assessment, professional recognition, fees, entry requirements and intakes can change. We confirm the latest details with Liverpool John Moores University before you apply. Global Educational Consultants is an independent consultancy.
MSc Data Science at Liverpool John Moores University | Fees & Entry | GEC