MSc Data Science
Learn how to apply technology to real-world data science problems and gain an in-depth understanding of emerging technologies, statistical analysis and computational techniques with this flexible master's degree in data science.
In-demand digital skills and knowledge
Learn how to apply technology to real-world data science problems and gain an in-depth understanding of statistical analysis and computational techniques. Acquire transferable skills that will help advance your career.
Specialise your degree
You have the option to study one of two specialist pathways. The artificial intelligence pathway may open up career opportunities in technology firms, robotics, military, academia, and public research sector, while Financial Technology can help you get a job in the financial sector.the military,
A mark of excellence
You’ll gain a prestigious qualification, respected by employers worldwide. The degree has been developed by Goldsmiths, University of London, one of the UK’s top institutions for innovation and creativity.
Study online anywhere in the world
Fit your studies around your commitments and pursue an internationally recognized degree without putting your life on hold. Continue to build your career momentum while gaining the knowledge and skills to unlock future opportunities. Benefit from comprehensive study materials written specifically for the degrees by leading experts.
Unlock a wealth of study resources
Access interactive computer sessions, study guides, past examination papers, and more via the Virtual Learning Environment (VLE). Receive personalized assignment feedback, tutorial support, discuss course material with other students through the online discussion forums, and take advantage of the personalized support at the local teaching center.
In the context of University of London Teaching Centres, academic direction refers to the design, development, and oversight of the academic content and standards of a programme. It is provided by a lead college (e.g., LSE, Royal Holloway, Goldsmiths), which ensures the quality and sets assessments. Teaching Centres like AIC deliver the teaching, but the academic authority and curriculum come from the University of London’s member institution directing the programme.
The University of London is the awarding body for all these programmes.
As a recognised Teaching Centre for University of London (UoL) programmes, AIC supports students studying for UoL degrees through distance and flexible learning.
AIC provides local, in-person academic support and resources to complement the materials and online platforms provided by UoL.
AIC offers face-to-face classes, tutorials, revision sessions, and access to facilities like libraries, computer labs, and study spaces.
AIC instructors are trained and familiar with UoL's academic standards and assessment requirements, helping students prepare effectively for exams and coursework.
While students are officially registered with the University of London and receive their degrees from the university itself, AIC, as a recognised Teaching Centre plays a role in delivering structured learning in a classroom environment, especially valuable for students who prefer guidance and interaction.
AIC, as a centre, may also assist with administrative processes such as registration, exam arrangements, and academic advising.
AIC, as a recognised Teaching Centres serves as a local partner in maintaining the quality and accessibility of the University of London’s global programmes.
The degree is available to be studied as a full master’s degree, a Postgraduate Diploma (PGDip) or a Postgraduate Certificate (PGCert).
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, with the exception of the Final Project.
You can also choose from one of two specialist pathways in:
Artificial Intelligence: MSc Artificial Intelligence | PGDip Artificial Intelligence
Financial Technology: MSc Financial Technology | PGDip Financial Technology - modules
The Programme Specification and Programme Regulations contain information and rules regarding what courses you can choose and the order in which they must be studied.
MSc: Four core modules, one compulsory module, five optional modules, plus a Final project (180 credits)
Postgraduate Diploma: Four core modules, one compulsory module and three optional modules (120 credits)
PGCert: Two core modules and two optional modules (60 credits).
Core modules
- Statistics and Statistical Data Mining
- Machine Learning
- Data Programming in Python
- Big Data Analysis
Compulsory modules
Optional modules
- Natural Language Processing (short course)
- Social Media and Network Science
- Artificial Intelligence
- R for Data Science
- Neural Networks
- Blockchain Programming
- Data Visualisation
- Financial Data Modelling
- Financial markets
- Mathematics for Data Science
Final Project (MSc only)
- Final Project (MSc)
MSc: Four core modules, three compulsory modules, three optional modules, plus a Final Project.
Core modules
Three compulsory modules
- Big Data Analysis
- Neural Networks
- Data Science Research Topics
Three optional modules
- Data Visualisation
- Natural Language Processing (short course)
- Social Media and Network Science
- R for Data Science
- Blockchain Programming
- Financial Data Modelling
- Financial markets
- Mathematics for Data Science
Final Project (MSc only)
- Final Project (MSc)
MSc: Four core modules, three compulsory modules, three optional modules, plus a Final Project.
Four Core modules
- Statistics and Statistical Data Mining
- Machine Learning
- Data Programming in Python
- Artificial Intelligence
Three compulsory modules
- Big Data Analysis
- Neural Networks
- Data Science Research Topics
Three Optional modules
- Data Visualisation
- Natural Language Processing (short course)
- Social Media and Network Science
- R for Data Science
- Blockchain Programming
- Financial Data Modelling
- Financial markets
- Mathematics for Data Science
Final Project (MSc only)
- Final Project (MSc)
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 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. Athens International College is a recognized teaching centres offering face to face tuition for Univeristy of London programmes.
Student Support
UoL are committed to delivering an exceptional student experience for all students regardless of which programmes you are studying.
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.
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.
Applications open |
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Applications close |
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Registration deadline |
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Programme starts |
October 2025 |
Examinations |
March 2026 |
To be announced
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.
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.
| October 2025 intake | |
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| Submit RPL request by | TBC |
| Submit supporting evidence by | TBC |
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.
Contact Athens International College for a quote.
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.
Professor Ioannis Chalikias
Professor
Emeritus Professor of Statistics
Dr Panagiotis Papoutsis
Adjunct Lecturer
Data Science Manager at Satori Analytics
Dr Stamatis Karlos
Adjunct Lecturer
Data Scientist at HP Inc.
Dr Pantelis Loupos
Assistant Professor
Assistant Professor at the School of Business, University of California, Davis
