)
Data Science at UEA
Find out more about studying Data Science at UEA, and browse our other courses.
Find out moreMSc Data Science (Part Time 3 Year)
Course
options
Key Details
- Attendance
- Part Time
- Award
- Degree of Master of Science
- Course Length
- 3 years
- Course Start Date
- September 2026
Other Start Dates
Chat to us on Unibuddy
Any questions? Chat online with current students, staff and experts. This is your chance to ask anything about UEA, university life, Norwich and more.
Other Start Dates
Scroll For More
Why you should choose us
=4th
in the UK for Computer Science for Graduate Prospects - Outcomes
The Complete University Guide 202492%
Of our research output is rated as "World-Leading" or "Internationally Excellent”
Research Excellence Framework (REF)Course Overview
Great business decisions are underpinned by high quality data. As a data scientist, you’re absolutely integral to the success of an organisation. You’ll source, analyse and utilise vast amounts of data to support strategic decision-making.
If that’s where you aspire to be, then this part-time MSc Data Science course is for you. You'll gain an advanced practical and theoretical grounding in data mining and statistics, with the chance to customise your degree through modules in artificial intelligence, visualisation, programming and database manipulation.
We have a large data science and statistics research group, which has made significant contributions to the field in the last 20 years, so you’ll be working directly with pioneering experts.
Data scientists are highly prized in almost any industry, so you’ll have great graduate career prospects as well as a wide range of transferable skills.
)
Study and Modules
Structure
On this course, you’ll take compulsory modules in research techniques, data mining, statistics and artificial intelligence or visualisation.
Alongside this, you’ll take optional modules from a range – which may include applications programming, database manipulation, human computer interaction, computer vision or a research topic.
Compulsory Modules
Optional A Modules
(Min Credits: 0, Max Credits: 40)Optional B Modules
(Min Credits: 0, Max Credits: 40)Important Information
Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring, review and update of modules. Where this activity leads to significant (but not minor) changes to programmes and their constituent modules, the University will endeavour to consult with students and others. It is also possible that the University may not be able to offer a module for reasons outside of its control, such as the illness of a member of staff. In some cases optional modules can have limited places available and so you may be asked to make additional module choices in the event you do not gain a place on your first choice. Where this is the case, the University will inform students.
Teaching and Learning
Teaching
As you'll be engaged in one module per semester with an average of four to five hours of contact time per week with teaching staff, depending on your module choices. This will be made up of a mixture of lectures, seminars and lab classes – where the lab and seminar classes reinforce and expand on the lecture material.
The course has both theoretical and practical elements, so you’ll get hands-on experience in data mining and statistical programming environments. You'll even have the opportunity to participate in commercial data mining projects as part of your assessment, gaining experience on all the stages of the KDD process.
Independent study
Your individual study (around seven hours per week) will complement formal teaching and will evolve along with your skills and expertise in data analysis. Beginning with an initial focus on the basics of programming and data manipulation, you’ll move on to much deeper study and appreciation of specialist topics such as data mining and statistics.
Your dissertation will also form a key part of your course, which will involve extensive independent study supported by your supervisor.
Assessment
We have a mixture of individual and group assessments. These include written work, presentations or demonstrations, and exams (closed and/or time-limited assessment). They combine theoretical understanding with practical application and are designed to test the range of skills and competencies required for the learning outcomes of each module. The balance of assessment types varies according to the options chosen.
Structure
Part-time students will take the required compulsory modules and will choose optional modules according to their programme's profile. However, there’ll be flexibility on which year students take particular modules, and choices will be made in conjunction with the academic adviser to ensure the best fit for work and other commitments.
Compulsory Modules
Optional A Modules
(Min Credits: 0, Max Credits: 40)Optional B Modules
(Min Credits: 0, Max Credits: 40)Important Information
Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring, review and update of modules. Where this activity leads to significant (but not minor) changes to programmes and their constituent modules, the University will endeavour to consult with students and others. It is also possible that the University may not be able to offer a module for reasons outside of its control, such as the illness of a member of staff. In some cases optional modules can have limited places available and so you may be asked to make additional module choices in the event you do not gain a place on your first choice. Where this is the case, the University will inform students.
Teaching and Learning
You'll take one module each semester, with an average of 4–5 hours of weekly contact time through lectures, seminars, and lab sessions. Seminars and labs are designed to build on lecture content, blending theory with practical experience in data mining and statistical programming. You'll also have the chance to work on commercial data mining projects, applying the full KDD process as part of your assessment.
In addition, you’ll spend around seven hours per week on independent study, starting with programming and data handling fundamentals before progressing to advanced topics like statistics and data mining. Your dissertation will be a major independent research project, guided by your supervisor.
Assessment
As per your first year, you’ll be assessed through a mix of individual and group tasks, including written assignments, presentations or demonstrations, and exams (either closed-book or time-limited). These assessments blend theory with practical application and are tailored to test the key skills and knowledge for each module. The types of assessments depend on the modules you choose. You’ll also complete an individual project, assessed through both a written report and a presentation or demonstration.
Structure
Part-time students will study the compulsory modules that are required for their degree, and they’ll also choose optional modules that are relevant to their programme of study.
A key element of the course is your dissertation, which will give you the chance to explore a topic or work on a problem (which may be with an industry partner) in depth, under the supervision of a member of faculty.
Recent dissertation titles include:
- Classification rule induction for atmospheric circulation patterns
- Keyword-based email classification
- Data analysis of orthopaedic operations
Compulsory Modules
Optional A Modules
(Min Credits: 0, Max Credits: 40)Optional B Modules
(Min Credits: 0, Max Credits: 40)Important Information
Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring, review and update of modules. Where this activity leads to significant (but not minor) changes to programmes and their constituent modules, the University will endeavour to consult with students and others. It is also possible that the University may not be able to offer a module for reasons outside of its control, such as the illness of a member of staff. In some cases optional modules can have limited places available and so you may be asked to make additional module choices in the event you do not gain a place on your first choice. Where this is the case, the University will inform students.
Teaching and Learning
Each semester, you’ll focus on a single module, with approximately four to five hours of weekly interaction with teaching staff. This includes a combination of lectures, seminars, and practical lab sessions, where seminars and labs enhance and build upon the lecture material. The course integrates both theoretical knowledge and practical skills, offering hands-on training in statistical programming and data mining. You may also contribute to real-world commercial data mining projects as part of your coursework, gaining practical experience across all stages of the KDD process.
You’ll also be expected to complete around seven hours of independent study each week. This self-guided learning supports and extends the taught content—starting with programming basics and data manipulation, then progressing to more advanced areas like statistics and data mining. Your dissertation is a significant element of the course and will involve in-depth, independent research under the guidance of a supervisor.
Assessment
Assessment is varied and includes both individual and group work. You'll complete written assignments, give presentations or demonstrations, and sit exams, which may be closed-book or time-restricted. These assessments are designed to evaluate both your theoretical knowledge and practical abilities, aligned with the learning outcomes of each module. The exact format and balance of assessments will depend on the modules you select. You'll also undertake a personal project, which will be evaluated through a written submission and a presentation or demonstration.
Employability
After the Course
You’ll graduate ready for a career in data analysis or data science – an area of rapid growth at the moment.
You can expect to earn a high salary – the median annual wage for data science in the UK was 60,000.
Careers
Examples of careers that you could enter include:
- Data scientist
- Data analyst
- Data miner
- Business intelligence analyst
Discover more on our Careers webpages.
Entry Requirements
- This course is open to
This course is open to UK applicants only. The annual intake for this course is in September each year .
- Typical UK Entry Requirements
Degree classification
Bachelors degree - 2.2.
Degree Subject
Computing, Mathematics or a related subject that evidences an ability in maths, statistics, data handling or database manipulation. You should be able to demonstrate some programming experience either in other qualifications or work experience.
- Admissions Policy
Our Admissions Policy applies to the admissions of all postgraduate applicants.
)
Fees and Funding
Tuition fees for the Academic Year 2026/27 are:
-
UK Students: £12,350
- International Students: £25,700
If you choose to study part-time, the fee per annum will be half the annual fee for that year, or a pro-rata fee for the module credit you are taking (only available for UK students).
Please note that this Part-Time (3 Year) course is not eligible for Student Loan funding.
We estimate living expenses at £1,136 per month.
Further Information on tuition fees can be found here.
Course Related Costs
Please see Additional Course Fees for details of additional course-related costs.
How to Apply
How to apply
Applications for Postgraduate Taught programmes at the University of East Anglia should be made directly to the University.
To apply please use our online application form.
Further information
If you would like to discuss your individual circumstances prior to applying, please do contact us:
Postgraduate Admissions Office
Tel: +44 (0)1603 591515
Email: admissions@uea.ac.uk