MSc Data Science

You can study this online degree from anywhere in the world. The flexible approach to learning enables you to fit your studies around your commitments whilst providing the academic rigour and structure of an on-campus programme.

Modules are offered over two 22-week sessions each academic year. You choose which sessions to enter and how many modules to take in each session.

Assessment deadlines are outlined clearly in advance of the session.

• The maximum number of modules you can study in one session is six, (or four plus the final project). You will also receive comprehensive learning materials and support from online tutors.

Study materials

We provide you with all of the resources and study materials you need to complete the course successfully, including the essential reading for each module. You can access these through the Virtual Learning Environment (VLE) on a range of devices.

Our online learning resources typically include multimedia content, activities and exercises (e.g. multiple choice quizzes, reflective exercises and self-assessment questions), as well as facilities for you to interact with your tutor and fellow students.

When you register with us, you will gain access to all resources and study materials via your Student Virtual Learning Environment (VLE), that will equip you to complete each module successfully. You will gain access to a range of multimedia content, activities, and exercises, as well as the opportunity to engage with your online tutor and fellow students.  

Online Library  

As a student at the University of London, you will have access to a range of resources, databases, and journals via the Online Library. You will be able to contact a team of professional and qualified librarians for any help you require.

Senate House Library

If you’re based in the United Kingdom, or are visiting London, make sure to visit Senate House Library. Students studying with the University of London can join the library free of charge. Membership includes a 10-book borrowing allowance, access to all reading rooms and study areas, and on-site access to Senate House Library digital resources.

Online tutor support

Studying our online MSc Data Science entitles you to receive tutor support and feedback. You will join an online tutor group to receive academic support and guidance on assessments. If you choose to study as a web-supported learner, you will have the opportunity to join an online tutor group and to engage with your fellow students. If you are interested in studying with a local teaching centre, you can benefit from face-to-face tuition.

All students receive tutor support and feedback while studying this programme. Tutors introduce the modules, respond to queries, monitor discussions and provide guidance on assessments.

Web-supported learning: if you register for a module as a web-supported learner, you join an online tutor group.

Institution-supported learning: if you enrol for a module with a local teaching centre, you receive face-to-face tuition. We work with several teaching centres in a number of countries and will recruit more to support the programme.

Student Support

We are committed to delivering an exceptional student experience for all of our students, regardless of which of our programmes you are studying and whether you are studying independently or with a Recognised Teaching Centre.

You will have access to support through:

• The Enquiry Hub – provides support for application and Student Portal queries.
• TalkCampus – a peer support service that offers a safe and confidential way to talk about whatever is on your mind at any time of day or night.

Time commitment

Study at your own pace, either part-time or full-time. Once you begin a module it is generally expected that you will complete it in the six-month session. Each module presents about 150 hours of study. Over a 22-week session, a 15 credit module will typically require five to seven hours of work/effort per week, and a 30 credit module will typically require ten to 15 hours of work/effort per week.

Assessment

Each module includes a mix of assessments. During your study period you will undertake formative assessments, which help you to measure your progress but do not count towards your grade, and summative assessments Summative assessments do count towards the final grade. These include a mid-session coursework submission and an unseen written examination (or final project) at the end of the session.

Written examinations are held twice a year. You can defer sitting an exam once (subject to a fee) but you cannot defer the submission of coursework.

More about exams

The academic content for the postgraduate Data Science degrees has been developed by the University of London with academic direction by the Department of Computing at Goldsmiths, University of London, one of the UK’s top creative universities.

Goldsmiths' unique hands-on project-based style works for a diverse range of interests – from computer and data science to art and music to social science and journalism.

Programme Director

Dr Tim Blackwell is a senior lecturer in Computer Science at Goldsmiths, University of London. Prior to his post at Goldsmiths, Tim was with the Open University, Edinburgh and Glasgow Universities and Imperial College, London. He trained as a theoretical physicist and computer scientist and researches a wide portfolio of subjects. Tim is best known for the creation of Swarm Music, an autonomous computer improviser. Much of his current work focuses on swarm intelligence algorithms and their use in problem solving. For example, he is currently researching swarm intelligent reconstructions of medical imaging acquisitions.

Tim is passionate about online and distance learning, and continuing education. He has delivered courses in a wide variety of subjects ranging from Quantum Philosophy to the Music of John Coltrane. Whilst at Goldsmiths he has led computer science and music computing modules across all undergraduate and postgraduate levels. In particular, he is leader of the Artificial Intelligence and Neural Networks modules.

Tim recently assumed the role of director of the MSc Data Science degree, which benefits from the input of Goldsmiths’ data science researchers, endeavours to deliver the essential cutting-edge and industry-standard techniques of this increasingly relevant discipline.

Entry routes

We offer two entry routes into the degrees, so if you do not meet the academic requirements you may still be eligible to apply through an alternative route.

Entry Route One (MSc/PGDip/PGCert) and individual modules

To be eligible to register for any of the Data Science degrees, you must have the following:

• A bachelor’s degree (or an acceptable equivalent) in a relevant subject which is considered at least comparable to a UK second class honours degree, from an institution acceptable to the University.
• Previous degrees should normally include a sufficient level of programming such as Python detailed in your transcript. Whilst other degrees such as Engineering and Mathematics will be considered on a case by case basis.
• If we consider your previous degree as non-relevant then we will request you take our MOOC, Foundations of Data Science: K-means Clustering in Python, before you start our Data Science programme. This MOOC requires approximately 30 hours of study.

Entry Route Two (MSc/PGDip/PGCert) and individual modules

A bachelor’s degree (or an acceptable equivalent) in any subject which is considered at least comparable to a UK second class honours degree, from an institution acceptable to the University.

• In addition to the above, you will be required to complete an online preparatory course prior to registration. The online preparatory course, Foundations of Data Science: K-Means Clustering in Python, requires approximately 30 hours of study.

English Language requirements

You need a high standard of English to study this programme. You will meet our language requirements if you have achieved one of the following within the past three years:

• IELTS: at least 6.5 overall, with 6.0 in the written test.
• TOEFL iBT: at least 92 overall, with 22+ in reading and writing and 20+ in speaking and listening.
• Cambridge Certificate of Proficiency in English.
• Cambridge Certificate of Advanced English (at grade C or above)
• Duolingo: must achieve an overall score of at least 120.

Alternatively, you may satisfy the language requirements if you have at least 18 months of education or work experience conducted in English.

Computer requirements

As this is a technical degree, you will need regular access to a computer with an internet connection and a minimum screen resolution of 1024x768. You will also need to view video material and have a media player (such as VLC) to play video files.

More about computer requirements

If you have studied material as part of a previous qualification that is comparable in content, level and standard to our Data Science modules, you may be exempted from the equivalent course of our degree. This is known as Recognition of Prior Learning (RPL) or Exemption. You will not need to study or be assessed in the module(s) to complete your award.

If you are registering on the following qualifications, you may be awarded RPL up to:

• MSc: 120 UK credits
• PGDip: 60 UK credits
• PGCert: 30 UK credits

RPL for the Final Project will not be considered.

To be considered for RPL you should make a formal request within your application when applying for the programme. Or, you can submit an online enquiry, if you have already applied.

You will need to have met the entrance requirements for the programme to be considered for RPL.

You must have completed the qualification/ examination(s), on which the application for RPL is based on, within the five years preceding the application.

We will not recognise or accredit prior learning for a module later than 14 days after the module start date. You will be deemed to have started a module once you have been given access to the learning materials on the VLE.

Automatic

Some qualifications are automatically recognised as meeting the learning outcomes of our courses. If you satisfy the conditions, we will accredit your prior learning as detailed here: Recognition of Prior Learning degrees in Data Science. No fees are charged for this service.

With the exception of the qualifications noted in the automatic RPL section on our website, applications for RPL based on examinations from professional institutions or professional certificates will not normally be considered.

Discretionary

Other qualifications will need to be assessed by specialist academics on a case by case basis, before we can approve RPL. A formal application is required and an RPL application fee is payable. The RPL application fee is non-refundable, even if your prior learning is not recognised.

Your qualification must be at the appropriate level (usually equivalent to a UK Level 7/ Master’s degree qualification or above) to be considered.

For your discretionary RPL request to be processed, you will need to provide: a completed RPL request form, the supporting documentary evidence (normally a scanned copy of an official transcript and syllabus of your previous studies) and the discretionary RPL fee.

You should apply as soon as possible so that we can process your request. You will need to allow time for academics to consider your documentation, so you can register by the registration deadline. 

All discretionary RPL requests must be submitted by the dates specified for the April or October session, in the year that you apply. We must receive all required supporting evidence by the deadline stated.

If you submit your discretionary RPL application but are too late to be considered for RPL in the current session, we will still process your application to study the programme. If you receive an offer, you can still register. If you wish to be considered for RPL in a subsequent session, then you shouldn’t register on the modules you want to apply for RPL.

How to request RPL

Additional Information about the process of applying for RPL can be found here.

Further information regarding RPL is covered in the Recognition of Prior Learning section of the appropriate Programme Regulations and Section 3 of the General Regulations.

The fee depends on two factors:

• Whether you choose web-supported or Recognised Teaching Centre supported learning.
• Whether you live in a developing (Band A) or developed (Band B) nation. See the list of Band A and Band B countries [PDF]

Important: the table below does not include fees payable to a third party, such as tuition costs payable to a Recognised Teaching Centre or fees charged by your local examination centre, or local VAT, Goods or Services Tax (GST) or sales tax.

The fees below relate to new students registering for the 2024-2025 session. On average, fees are subject to a five per cent year-on-year increase.

Students who registered earlier can view their fees on the Course Fees page.

Disclaimer: Currency conversion tool

*The indicative totals given represent the amount you would expect to pay if you were to complete the MSc degree / PGDip / PGCert in the minimum period of time (one year, subject to module availability), without resits, and with a year-on-year increase of five per cent. These totals do not reflect the cost of any additional tuition support you may choose to take or the fee levied by your local examination centre.

*The online examination administration fee is charged for each examination paper held online, including resits. This does not apply to any coursework submissions.

How fees work

Your fees include study materials and entry into assessments.

The indicative programme fee includes all module and continuation fees for the duration of your study, as well as online tutor support.

With pay per module, you pay for each module as you register for it. The 'web-supported learning' fee includes support from a University of London online tutor. Alternatively, if you prefer face-to-face tuition, you can pay a smaller fee to us and a separate fee to a teaching centre which supports the programme.

The module continuation fee is the cost per module if you defer an examination or need to retake assessments. It includes all study materials, entry into assessments, and online tutor support.

Additional costs

You will also need to budget for:

• Exams: our approved examination centres around the world charge a fee when you sit an exam. Contact your chosen examination centre for details about costs.
• Tuition: as described, teaching centres charge face-to-face tuition if you choose to take modules with institution-supported learning.
• Recognition of prior learning applications: these are not included with the course fees.

Some fees are non-refundable. Please see the refund and compensation policy for further details.

Sales Tax

Please note: All student fees are net of any local VAT, Goods and Services Tax (GST) or any other sales tax payable by the student in their country of residence. Where the University is required to add VAT, GST or any other sales tax at the local statutory rate, this will be added to the fees shown during the payment process. For students resident in the UK, our fees are exempt from VAT.

Further information on Sales Tax.

Without the cost of moving to London, studying for your University of London degree anywhere in the world represents excellent value for money. However, there are additional sources of support depending on where you live and how you choose to study.

If you are a UK or EU national and you have lived in England for three years, you could be eligible to apply for a Postgraduate Loan.

More on funding your study.

Scholarships

The Aziz Foundation Scholarship Programme offers four Master’s scholarships for the 2023-24 academic year.

Can I get sponsored?

If you're employed, your employer may be willing to cover part/all of the programme fees if you can make a compelling case as to how this programme will boost your contribution to the workplace.

Our courses are ideal for employers because they get to retain you as an employee and benefit from your learning from the moment you begin.

How can I get sponsored by my employer?

You can pay your fees in a number of ways, including an online payment facility via the Student Portal and Western Union Quick Pay.

More on how to pay your fees

Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government.

Our flexible degree addresses the skills shortage of data scientists who can use data to drive improvements to organisational performance. You will have the opportunity to gain highly-valued skills through the specialist pathways:

MSc Data Science
These skills will lead to a variety of careers with employers from technology firms, the biomedical research sector, the charitable and voluntary sector, and public research sector.

MSc Data Science and Artificial Intelligence
Embark on a variety of careers with employers from leading technology firms, robotics, military, academia, and public research sector.

MSc Data Science and Financial Technology
For a variety of careers with employers from the financial sector, including financial planning, insurance, marketing, and investment banking.

What do employers think of our graduates?

In some countries, qualifications earned by distance and flexible learning may not be recognised by certain authorities or regulators for the purposes of public sector employment or further study. We advise you to explore the local recognition status before you register, even if you plan to receive support from a local teaching centre.

You’ll have access to a wide range of careers and employability support through the University of London Careers Service, including live webinars and online drop-in sessions.

More on the University of London Careers Service