Data Science (FinTech), MSc
Our MSc degree in Data Science (FinTech) is designed to provide you with an endorsed qualification in the specialist area of Data Science with Financial Technology.
You will gain a solid grounding in Data Science coupled with a concrete introduction to Financial Technology, arming you with the knowledge vital for employment in the FinTech space, where expertise in Data Science and AI is in extremely high demand.
Data Science and AI are at the core of modern Finance, with Financial Technology being its driving force. There is a growing, uninterrupted demand for specialists with the technical and practical skills to design, architect, and engineer systems and models for use in areas including Investment Analysis, Algorithmic Trading, Risk Management, Decentralised Payment Systems, Fraud Detection, and Anti-Money Laundering, to name a few. Skills in Blockchain Design are furthermore critical with the growth in Stablecoin and Central Bank Digital Currency technologies.
In addition to offering you solid, in-depth exposure to the principles and practices of Data Science, the MSc Data Science (FinTech) programme provides you with the opportunity to familiarise yourself with data-driven expertise in the world of payments and transactions, as well as fraud and anti-money laundering detection technologies, which are all inherent in regulation-compliant exchanges, including those involving decentralised assets, cryptocurrencies, and stablecoins. Fundamentals and principles of blockchain and its applications to FinTech use cases, as well as techniques for data-driven anti-money laundering, form a solid part of the curriculum. Meanwhile, MSc Data Science (FinTech) scholars are offered the opportunity to complete their Master's thesis on FinTech topics.
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Location
Duration
- 1 years full-time
- 2 years part-time
Start month
September; January
Home /international fees 2024/25
£11,000 /£18,150
What you should know about this course
What you will study
Full time
Year 1
Students are required to study the following compulsory modules.
- MSc Project (60 credits)
- Big Data (15 credits)
- Data Visualisation (15 credits)
- Machine Learning (15 credits)
- Applied Machine Learning (15 credits)
- Blockchain for FinTech Applications (15 credits)
- Technologies for Anti-Money Laundering and Financial Crime (15 credits)
- Programming Fundamentals for Data Science (15 credits)
- Essential Professional and Academic Skills for Masters Students
- Statistical Methods for Time Series Analysis (15 credits)
Part time
Year 1
Students are required to study the following compulsory modules.
- Big Data (15 credits)
- Machine Learning (15 credits)
- Applied Machine Learning (15 credits)
- Programming Fundamentals for Data Science (15 credits)
- Essential Professional and Academic Skills for Masters Students
Year 2
- MSc Project (60 credits)
- Data Visualisation (15 credits)
- Blockchain for FinTech Applications (15 credits)
- Technologies for Anti-Money Laundering and Financial Crime (15 credits)
- Statistical Methods for Time Series Analysis (15 credits)
About the course team
The programme team are experienced academic and industry professionals, with many years of experience in various aspects and applications of Data Science. Our teaching is informed by research and consultancy work, as well as by the latest teaching best practice.
Come and meet us
We are offering virtual events so that you can still experience how Greenwich could be the right university for you.
Next Open Days
Got a question?
To find out more about our Open Days and Campus Tours or if you need any assistance, please email opendays@gre.ac.uk.
Entry requirements
You should hold an undergraduate (honours) degree at 2:2 or above in a computer science, AI, data science, or a relevant STEM subject (e.g. physics, engineering, mathematics, statistics, IT)
OR substantial commercial/industrial experience including software development using modern programming languages and design.
Applicants who do not hold an undergraduate degree in computer science, AI, data science, or a relevant STEM subject, should consider MSc Data Science and its Applications, a specialist course designed for applicants from any background.
For more information, use our contact form or call us on 020 8331 9000.
You can also read our admissions policy.
Available to overseas students?
Yes
Can I use Prior Learning?
Find out more on our Recognition of Prior Learning pages.
How you will learn
Teaching
In a typical week, learning takes place through a combination of lectures, tutorials and practical work in the labs. You'll be able to discuss and develop your understanding of topics covered in lectures in smaller group sessions, and apply this knowledge in practice in the specialised computer laboratories.
Teaching hours may fall between 9am and 9pm, depending on your elective courses and tutorials.
Class sizes
Lectures are usually attended by larger groups and seminars/tutorials by smaller groups. This can vary more widely for modules that are shared between degrees.
Independent learning
Outside of timetabled sessions, you'll need to dedicate time to self-study to complete coursework, and prepare for presentations and exams. Our Stockwell Street library and online resources will support your further reading and research.
You can also join a range of student societies, including our Computer and Technology Society, Gre Cyber Sec, Forensic Science Society, and Games Development Society.
Overall workload
Your overall workload consists of lectures, tutorials, labs, independent learning, and assessments. For full-time students, the workload should be roughly equivalent to a full-time job. For part-time students, this will reduce in proportion with the number of modules you are studying.
Assessment
On this course, students are assessed by coursework, examinations and a project. Some modules may also include 'practice' assessments, presentations, demonstrations, and reports, which help you to monitor progress and make continual improvement.
Feedback summary
We aim to give feedback on assignments within 15 working days.
Dates and timetables
The academic year runs from September to the end of August, as the students are working on their project full-time during the summer months.
Full teaching timetables are not usually available until term has started. For any queries, please call 020 8331 9000.
Fees and funding
University is a great investment in your future. English-domiciled graduate annual salaries were £10,500 more than non-graduates in 2023 - and the UK Government projects that 88% of new jobs by 2035 will be at graduate level.
(Source: DfE Graduate labour market statistics: 2023/DfE Labour market and skills projections: 2020 to 2035).
| Cohort | Full time | Part time | Distance learning |
|---|---|---|---|
| Home | £11,000 | £1,850 per 30 credits | N/A |
| International | £18,150 | £3,025 per 30 credits | N/A |
Accommodation costs
Whether you choose to live in halls of residence or rent privately, we can help you find what you're looking for. University accommodation is available from £126.35 per person per week (bills included), depending on your location and preferences. If you require more space or facilities, these options are available at a slightly higher cost.
Scholarships and bursaries
We offer a wide range of financial help including scholarships and bursaries.
The Greenwich Bursary
This bursary is worth £700 for new undergraduate students with a low household income, entering Year 0 or 1 who meet the eligibility criteria.
EU Bursary
Following the UK's departure from the European Union, we are supporting new EU students by offering a substantial fee-reduction for studying.
Financial support
We want your time at university to be enjoyable, rewarding, and free of unnecessary stress, so planning your finances before you come to university can help to reduce financial concerns. We can offer advice on living costs and budgeting, as well as on awards, allowances and loans.
If there are any field trips, students may need to pay their own travel costs.
Careers and placements
What sort of careers do graduates pursue?
Graduates from this Computer Science course are equipped for employment in industry, commerce or research with a proficiency in the key theoretical and practical areas of data science, including their application to modern financial technologies and artificial intelligence systems.
Our Employability and Careers services provides support and help the students to achieve their potential and support their transition towards a rewarding graduate career, including CV clinics, mock interviews, and employability skills workshops.
Do you provide employability services?
Our services are designed to help you achieve your potential and support your transition towards a rewarding graduate career.
The Employability and Careers Service provides support when you are preparing to apply for placements and graduate roles. It includes CV clinics, mock interviews and employability skills workshops.
Each School also has its own Employability Officer, who works closely with the industry and will provide specific opportunities relevant to your own course.
More about Careers.
Support and advice
Academic skills and study support
We want you to make the most of your time with us. You can access study skills support through your tutor, lecturers, project supervisor, subject librarians, and our academic skills centre.
We provide additional support in Mathematics.
Support from the department
As a student in the School of Computing and Mathematical Science, you will be able to enter our Oracle mentoring scheme. This helps you liaise with industry for advice on careers, professional insight, job-hunting, and you'll also develop skills to boost your employability.
Not quite what you were looking for?
We've got plenty of other courses for you to choose from. Browse our postgraduate courses or check our related courses below.....
Computer science at the University of Greenwich
Discover the possibilities of technology at Greenwich, where courses shape students from undergraduate to postgraduate levels. Covering some of the most relevant industry topics, such as cybersecurity and artificial intelligence.
Visit our computer science degrees page.
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