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Students
Tuition Fee
GBP 18,700
Per year
Start Date
2025-09-01
Medium of studying
On campus
Duration
12 months
Program Facts
Program Details
Degree
Masters
Major
Artificial Intelligence | Data Science | Software Engineering
Area of study
Information and Communication Technologies
Education type
On campus
Timing
Full time
Course Language
English
Tuition Fee
Average International Tuition Fee
GBP 18,700
Intakes
Program start dateApplication deadline
2025-09-01-
2025-01-01-
About Program

Program Overview


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.


School

Computing and Mathematical Sciences


Location

Greenwich Campus


Duration

  • 1 years full-time
  • 2 years part-time

Start month

  • September
  • January

Home/international fees 2025/26

  • £11,325 /£18,700

What you should know about this course

  • Study a course tailored to transform professionals and practitioners with Computer Science background into accomplished Data Scientists in the growing FinTech industry.
  • Become a data-driven finance and technologist specialist in an area of need.
  • Gain technical and practical skills in how to use technology in investment analysis, algorithmic trading, risk management, payment and fraud detection.

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)
  • Blockchain for FinTech Applications (15 credits)
  • Technologies for Anti-Money Laundering and Financial Crime (15 credits)
  • Programming Fundamentals for Data Science (15 credits)
  • Ethics in Data Science (15 credits)
  • Financial Machine Learning (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)
  • Programming Fundamentals for Data Science (15 credits)
  • Ethics in Data Science (15 credits)
  • Financial Machine Learning (15 credits)
  • Essential Professional and Academic Skills for Masters Students

Year 2

Students are required to study the following compulsory modules.


  • 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)

Entry requirements

UK citizens and permanent residents

An undergraduate (honours) degree at 2:2, or above, in Computing, Computer Science, AI, Data Science, Mathematics, Physics, Engineering, Statistics, IT or a relevant STEM subject.


Applicants without a degree that have substantial commercial/industrial experience including software development using modern programming languages and design may be considered.


Applicants with a degree in another discipline should consider MSc Data Science, a specialist course designed for applicants from any degree background.


International entry requirements

The University of Greenwich accepts a broad range of international qualifications for admission to our courses.


For detailed information on the academic and English language requirements, please find your country in our directory.


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.


Fees and funding

Fees

  • Home: £11,325
  • International: £18,700

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.


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.


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.


Program Outline


Data Science (FinTech), MSc - University of Greenwich


Degree Overview:

This MSc degree in Data Science (FinTech) is designed to provide students with an endorsed qualification in the specialist area of Data Science with Financial Technology. The program aims to equip students with the knowledge and skills necessary for employment in the FinTech space, where expertise in Data Science and AI is in high demand. The program emphasizes the growing importance of Data Science and AI in modern Finance, particularly within Financial Technology. It highlights the need for specialists with the technical and practical skills to design, architect, and engineer systems and models for use in areas such as:

  • Investment Analysis
  • Algorithmic Trading
  • Risk Management
  • Decentralised Payment Systems
  • Fraud Detection
  • Anti-Money Laundering
  • The program also emphasizes the critical role of Blockchain Design skills in the context of Stablecoin and Central Bank Digital Currency technologies. Beyond providing a solid foundation in Data Science principles and practices, the MSc Data Science (FinTech) program offers students the opportunity to gain data-driven expertise in:
  • Payments and transactions
  • Fraud and anti-money laundering detection technologies
  • Blockchain and its applications to FinTech use cases
  • Data-driven anti-money laundering techniques
  • Students are also given the opportunity to complete their Master's thesis on FinTech topics.

Outline:


Subject to validation:

The program is currently subject to the final stage of approval and validation. In the unlikely event that the course is not approved before the start date, the university will assist students in finding an alternative course.


Indicative Modules:

  • MSc Project (60 credits): This module allows students to apply their knowledge and skills to a real-world FinTech problem.
  • Big Data (15 credits): This module covers the principles and techniques of big data management and analysis.
  • Machine Learning (15 credits): This module introduces students to the fundamental concepts and algorithms of machine learning.
  • Applied Machine Learning (15 credits): This module provides practical experience in applying machine learning techniques to real-world problems.
  • Programming Fundamentals for Data Science (15 credits): This module covers the essential programming skills required for data science, including Python and R.
  • Essential Professional and Academic Skills for Masters Students: This module equips students with the necessary skills for academic success and professional development.
  • Statistical Methods for Time Series Analysis (15 credits): This module focuses on the analysis of time-series data, a crucial aspect of financial data analysis.

Assessment:

The program utilizes a combination of coursework, examinations, and a project for assessment. Some modules may also include practice assessments, presentations, demonstrations, and reports to monitor student progress and facilitate continuous improvement.


Teaching:

The program is taught by experienced academic and industry professionals with extensive experience in various aspects and applications of Data Science. The teaching is informed by research and consultancy work, as well as by the latest teaching best practices.


Careers:

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. The University's Employability and Careers services provide support to help students achieve their potential and transition towards a rewarding graduate career. These services include:

  • CV clinics
  • Mock interviews
  • Employability skills workshops
  • Each School also has its own Employability Officer who works closely with the industry to provide specific opportunities relevant to each course.

Other:

  • The program is offered on the Greenwich Campus.
  • The program duration is 1 year full-time or 2 years part-time.
  • The program starts in September or January.
  • The program is available to overseas students.
  • Students can use Prior Learning to gain credit for previous studies.
  • The program includes a combination of lectures, tutorials, and practical work in labs.
  • Students are expected to dedicate time to self-study outside of timetabled sessions.
  • Students can join various student societies, including the Computer and Technology Society, Gre Cyber Sec, Forensic Science Society, and Games Development Society.
  • The overall workload for full-time students is equivalent to a full-time job.
  • The workload for part-time students is reduced proportionally to the number of modules studied.
  • Feedback on assignments is provided within 15 working days.
  • The academic year runs from September to the end of August, with students working on their project full-time during the summer months.
  • Full teaching timetables are usually available at the start of the term.
  • Students may need to pay their own travel costs for field trips.
  • The University offers advice on living costs, budgeting, awards, allowances, and loans.
  • EU students may be eligible for a bursary to support their studies.
  • The University provides advice and support on budgeting, money management, and financial hardship.
  • Accommodation is available from £126.35 per person per week (bills included), depending on location and preferences.

Tuition Fees:

  • Home/international fees 2024/25: £11,000 // £18,150
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University of Greenwich: A Summary


Overview:

The University of Greenwich is a public university located in London and Kent, England. It boasts three campuses: Greenwich, Avery Hill, and Medway. The university is known for its diverse student body, with students from over 150 countries, and its commitment to providing a high-quality student experience.


Services Offered:

The University of Greenwich offers a wide range of services to its students, including:

    Accommodation:

    On-campus accommodation options are available at all three campuses.

    Careers:

    The university provides career guidance and support services to help students find employment after graduation.

    Student Support:

    A variety of support services are available to students, including academic advising, counseling, and disability support.

    Financial Aid:

    Scholarships and bursaries are available to help students finance their studies.

    Digital Student Centre:

    A digital platform offering support for new and returning students.

Student Life and Campus Experience:

The University of Greenwich offers a vibrant and diverse campus experience. Students can expect:

    Lively Students' Union:

    Each campus has a Students' Union that organizes social events, clubs, and societies.

    Modern Facilities:

    The university has invested in modern facilities, including libraries, labs, and sports centers.

    Excellent Transport Links:

    All campuses are easily accessible by public transport, with connections to central London.

    Campus Bus Service:

    A bus service connects the three campuses.

Key Reasons to Study There:

    Award-Winning Research:

    The university is recognized for its high-quality research, which has won numerous awards.

    Gold in the Teaching Excellence Framework (TEF):

    This recognition highlights the university's commitment to providing an outstanding student experience.

    Diverse Community:

    The university welcomes students from all over the world, creating a diverse and inclusive learning environment.

    Flexible Learning Options:

    The university offers a range of flexible learning options, including online and part-time study.

    Strong Graduate Prospects:

    The university has a strong track record of graduate employment, with many graduates going on to successful careers.

Academic Programs:

The University of Greenwich offers a wide range of undergraduate and postgraduate programs across various disciplines. Some of the key academic strengths include:

    Business and Management:

    The university is known for its strong business programs, including MBA and MSc programs.

    Engineering and Technology:

    The university offers a range of engineering and technology programs, including civil engineering, mechanical engineering, and computer science.

    Arts and Humanities:

    The university has a strong reputation in the arts and humanities, with programs in English literature, history, and creative writing.

    Health and Social Care:

    The university offers a range of health and social care programs, including nursing, social work, and psychology.

Other:

  • The university has a strong commitment to sustainability and has launched a university-wide transformation for a Greener future.
  • The university is home to the Greenwich Portraits series, which celebrates the diverse journeys of its students and alumni.

  • Student Life and Campus Experience:

    While the context mentions the Students' Union and facilities, it does not provide detailed information on student life and campus experiences.

  • Key Reasons to Study There:

    The context mentions some advantages, but it does not explicitly highlight the key reasons to study at the University of Greenwich.

Total programs
372
Admission Requirements

Entry Requirements:

  • UK citizens and permanent residents:
  • 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.
  • International entry requirements:
  • Alternatively, please contact us:
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