Data Science

MSc: Four core modules, two compulsory modules, four optional modules, plus a final project. (180 credits)
Postgraduate Diploma: Four core modules, two compulsory modules and two optional modules. (120 credits)
Post Graduate Certificate: Two core modules and two optional modules. (60 credits)
Individual modules: There is provision for individual modules to be studied and assessed on a stand-alone basis without being registered for a related qualification. You may register for any number of core or optional modules on a stand-alone basis (subject to module availability), with the exception of the Final Project.

View the modules for MSc Data Science

View MSc Data Science module release dates [PDF]

• Download the Programme Specification MSc [PDF]

You can also choose from one of two specialist pathways in:

Artificial Intelligence:

MSc Artificial Intelligence - modules
PGDip Artificial Intelligence - modules

Financial Technology:

MSc Financial Technology - modules
PGDip Financial Technology - modules

• View Programme Regulations

This programme has two intake dates per year: April and October.

You can study this online programme 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.

Online support

When you register, we will give you access to your Student Portal. You can then access your University of London email account and other key resources:

• On the VLE you can access electronic copies of all printed study materials, resources including audio-visual, and forums to discuss course material and work collaboratively with others.
• The Online Library provides access to over 100 million academic electronic items comprising E-books, E-journals, conference proceedings, etc. In addition, students can request items which are not held in the library via the library's Inter-Library loans service with the British Library.
• Senate House Library provides free reference access for all registered distance and flexible learning students.
• Access to academic support and feedback from London-based support teams. Tutors introduce the modules, respond to queries, monitor discussions and provide guidance on assessments.

If you register for support at one of our recognised teaching centres you can attend lectures and benefit from, and receive tutor support.

Tutor support

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 Student Advice Centre – 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.
• Student Relationship Managers - a team of Student Relationship Managers (SRMs) are here to support and advise you throughout your studies. They aim to ensure that you are fully up-to-date with important and useful information about how best to complete your studies.

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.

What qualifications do you need?

Entry routes

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

Entry Route 1 (MSc/PGDip/PGCert) and individual modules

To be eligible to register for any of the Data Science programmes, 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 2 (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 Adobe Flash Player to view video material and 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 MSc Data Science modules, you might be exempted from the equivalent course of our degree. This process 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.

To be considered for RPL you should make a formal request within your application when applying online. Alternatively, this can be done through an online enquiry, once you have submitted your application.

Depending on which route you register for you can potentially apply for RPL for up to 120 credits.

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.

More about RPL and the application process

Your qualifications will need to be assessed by specialist academics on a case by case basis, before we can approve recognition and accreditation of Prior Learning. This is called discretionary RPL and involves a non-refundable application fee.

You will need to be eligible to study the programme and will need to provide the following for your RPL application to be considered: a completed RPL request form, the supporting documentary evidence (normally a transcript and syllabus of your previous studies) and the fee payable.

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

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

Automatic RPL

Some qualifications are automatically recognised as meeting the learning outcomes of our programmes. If you satisfy the conditions, make a formal request and supply the necessary evidence, we will accredit your prior learning as detailed here: Recognition of Prior Learning degrees in Data Science. No fees are charged for this service.

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 programme fees below refer to the October 2022 and April 2023 sessions only and are effective from 1 January 2022.

*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 5%. 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.

More about programme fees.

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.

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.

How to pay your fees.

Your payment provider may apply additional transaction fees (if in doubt, please check with them before making a payment).

Further information about fee payment options can be found in the “How to pay your fees” link above.

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

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

The benefits of our programme is flexible to address 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.

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.

The academic content for the postgraduate Data Science programmes 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

Professor Robert Zimmer is the Head of the Department of Computing at Goldsmiths. Robert specialises in AI, theorem proving with applications to hardware/software design & verification, safety-critical systems, humanities computing, and digital multimedia with applications.

Studying for your University of London degree from anywhere in the world without the costs of relocating represents excellent value for money. However, there may be additional sources of support depending on where you live and how you choose to study.

More on funding your study

Can I get sponsored?

If you’re working and apply to do this degree, your employer may be willing to help with the cost. Our online programmes are ideal for employers, because they keep you as an employee, while benefiting from the additional skills you bring to the workplace.

More on employer sponsorship

We have a template available to help you present a case to your employer.

• Employer sponsorship business case template [PDF]