MSc Data Science (Part Time 2 Year)
Key Details
- Attendance
- Part Time
- Award
- Degree of Master of Science
- Course Length
- 2 years
- Course Start Date
- September 2024
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Course Overview
Great business decisions are underpinned by high quality data. As a data scientist, you are 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 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.
Organisations today have a vast amount of raw data generated from their computerised operational systems. So how will they turn this into high quality information for strategic decision-making? They need a new generation of data analysts who understand effective and efficient data analysis methods and the Knowledge Discovery and Data Mining (KDD) process.
This MSc Data Science Part Time course – one of the most established in this area with over 15 years of history – offers an excellent platform to help you forge a successful career in data analysis.
As a student, you’ll be part of our vibrant research community and will have very good opportunities to progress to a PhD. You’ll be part of a research group that has made significant contributions in techniques for data mining and KDD – including KDD Methodologies; use of metaheuristics for rule and tree induction; all-rule induction; clustering techniques; feature subset selection; feature construction; time series classification as well as many applications in the financial services industry, medicine and telecommunications.
The research group has collaborated in research or consultancy projects with a wide range of organisations, including: the Biotechnology and Biological Sciences Research Council (BBSRC), the Engineering and Physical Sciences Research Council (EPSRC), the Institute and Faculty of Actuaries and The Royal Society, Alston Transport, Derbyshire Police, Lanner Group, Master Foods, MET Office, National Air Traffic Services, Aviva, Process Evolution Ltd, Simultec AG Zurich, Virgin Money and the Norwich Football Club.
You’ll graduate with a wealth of knowledge, prestigious connections and research experience – putting you one step ahead of other graduates in your career or further studies.
Module enrolment for each year is discussed with the Course Director at the start of the course.
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 two optional modules from a range – which may include applications programming, database manipulation, human computer interaction, computer vision or a research topic.
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:
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Classification rule induction for atmospheric circulation patterns
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Keyword-based email classification
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Data analysis of orthopaedic operations
Compulsory Modules
Optional A Modules
(Min Credits: 0, Max Credits: 40)Optional B Modules
(Credits: 40)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
As you’ll be engaging in one to two modules per semester, you’ll have an average of between 4 and 9 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 7 to 14 hours per week) will complement the 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. Additionally, there is an individual project which is assessed through a combination of written work and demonstration or presentation.
Structure
Part-time students will take the required compulsory modules and will choose optional modules according to their programme's profile. However, there will 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 to work and other commitments.
Compulsory Modules
Optional A Modules
(Min Credits: 20, Max Credits: 40)Optional B Modules
(Credits: 40)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
As you’ll be engaging in one to two modules per semester, you’ll have an average of between 4 and 9 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 7 to 14 hours per week) will complement the 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 offer a variety of assessments, including individual and group work. These assessments 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. Additionally, there is an individual project which is assessed through a combination of written work and demonstration or presentation.
The specific assessments that you’ll be required to complete will vary depending on the module that you are taking. However, all of our assessments are designed to help you develop the skills and knowledge that you need to succeed in your studies.
Entry Requirements
- Degree Classification
- Bachelors degree 2.1 or equivalent
- Degree Subject
- Computing, Mathematics or a related subject. You should be able to demonstrate some programming experience either in other qualifications or work experience.
- English Foreign Language
Applications from students whose first language is not English are welcome. We require evidence of proficiency in English (including writing, speaking, listening and reading):
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IELTS: 6.5 overall with 6.0in all components
Test dates should be within 2 years of the course start date.
We also accept a number of other English language tests. Review our English Language Equivalencies for a list of qualifications that we may accept to meet this requirement.
If you do not yet meet the English language requirements for this course, INTO UEA offer a variety of English language programmes which are designed to help you develop the English skills necessary for successful undergraduate study:
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- Intakes
This course is open to UK and International applicants. The annual intake for this course is in September each year .
Additional Information or Requirements
Admissions Policy
Our Admissions Policy applies to the admissions of all postgraduate applicants.
Fees and Funding
Tuition fees for the Academic Year 2024/25 are:
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UK Students: £11,000 (full time)
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International Students: £22,900 (full time)
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 Home students).
We estimate living expenses at £1,023 per month.
Further Information on tuition fees can be found here.
Scholarships and Bursaries
The University of East Anglia offers a range of Scholarships; please click the link for eligibility, details of how to apply and closing dates.
Course Related Costs
Please see Additional Course Fees for details of additional course-related costs.
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
International candidates are also encouraged to access the International Students section of our website.
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
A degree at UEA will prepare you for a wide variety of careers. We've been ranked 1st for Job Prospects by StudentCrowd in 2022.
Examples of careers that you could enter include:
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Data scientist
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Data analyst
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Data miner
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Business intelligence analyst
Discover more on our Careers webpages.