Data Science brings together computational and statistical skills and machine learning for data-driven problem solving. This rapidly expanding area includes deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.
Modes and duration
Tuition fees (2020/21)
Note on fees: The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website.
Fee deposit: All full time students are required to pay a fee deposit of £2,000 for this programme. All part-time students are required to pay a fee deposit of £1,000.
A minimum of an upper second-class Bachelor's degree in a quantitative discipline (such as mathematics, computer science, engineering, physics or statistics) from a UK university or an overseas, qualification of an equivalent standard. Knowledge of mathematical methods including linear algebra, calculus, probability and statistics at first-year university level is required. Depending on the modules selected, students undertake assignments that contain programming elements and prior experience in a high-level programming language (R/matlab/python) is useful. Relevant professional experience will also be taken into consideration.
English language requirements
If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.
The English language level for this programme is: Good
Further information can be found on our English language requirements page.
Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.
International applicants can find out the equivalent qualification for their country by selecting from the list below.
Select your country:
About this degree
The programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a range of industry partners.
Students undertake modules to the value of 180 credits.
The programme consists of three compulsory modules (45 credits), two to three optional modules (30 to 45 credits), two to three elective modules (30 to 45 credits) and a dissertation (60 credits).
Upon successful completion of 180 credits, you will be awarded a MSc in Data Science and Machine Learning.
Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change.
- Applied Machine Learning (15 credits)
- Introduction to Machine Learning (15 credits)
- Introduction to Statistical Data Science (15 credits)
- MSc Data Science and Machine Learning Project (60 credits)
Students must choose 30 to 45 credits from the optional modules, and 30 to 45 credits from elective modules.
- Options (choose 30 to 45 credits)
- Information Retrieval and Data Mining (15 credits)
- Introduction to Deep Learning (15 credits)
- Machine Vision (15 credits)
- Multi-agent Artificial Intelligence (15 credits)
- Reinforcement Learning (15 credits)
- Statistical Natural Language Processing (15 credits)
- Electives (choose 30 to 45 credits)
- Affective Computing and Human-Robot Interaction (15 credits)
- Applied Bayesian Methods (15 credits)
- Bioinformatics (15 credits)
- Computational Modelling for Biomedical Imaging (15 credits)
- Decision and Risk (15 credits)
- Forecasting (15 credits)
- Graphical Models (15 credits)
- Robot Vision and Navigation (15 credits)
- Robotic Systems Engineering (15 credits)
- Statistical Design of Investigations (15 credits)
- Supervised Learning (15 credits)
Please note: the availability and delivery of modules may vary, based on your selected options as all choices are subject to timetabling constraints
- Further information about these modules is available on the department website.
All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.
Teaching and learning
The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.
For more information about funding opportunities for Department of Computer Science postgraduate programmes, please see the departmental Scholarships and Funding pages: www.ucl.ac.uk/computer-science/scholarships-and-funding
Scholarships relevant to this department are displayed below.
- Deadline: 14 May 2020
- £15,000 (1 year)
- Based on both academic merit and financial need
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. This is a very new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:
- Google Deepmind
- Microsoft Research
- Index Ventures
- Deutsche Bank
- Morgan Stanley
Students gain a thorough understanding of the fundamentals required from the best practitioners, and the programme's broad base enables data scientists to adapt to rapidly evolving goals.
Why study this degree at UCL?
UCL received the highest percentage (96%) for quality of research in Computer Science and Informatics in the UK's most recent Research Excellence Framework (REF2014).
UCL Computer Science staff have research interests ranging from foundational machine learning and large-scale data analysis to commercial aspect of business intelligence. Our extensive links to companies provide students with opportunities to carry out the research project with an industry partner.
The department also enjoys strong collaborative relationships across UCL; exposure to interdisciplinary research spanning UCL Computer Science and UCL Statistical Science will provide students with a broad perspective of the field. UCL is home to regular machine learning masterclasses and big data seminars.
Department: Computer Science
Application and next steps
Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
There is an application processing fee for this programme of £80 for online applications and £105 for paper applications. Further information can be found at: www.ucl.ac.uk/prospective-students/graduate/taught/application.
- All applicants
- 12 June 2020
Due to competition for places on this programme, no late applications will be considered.
For more information see our Applications page.Apply now
What are we looking for?
When we access your application we would like to learn:
- why you want to study Data Science at graduate level
- why you want to study Data Science at UCL
- what particularly attracts you to the chosen programme
- how your academic and professional background meets the demands of this challenging programme
- where you would like to go professionally with your degree
Please preface your personal statement with a note on modules taken which strongly relate to: Linear Algebra, Calculus, Probability and Statistics. Together with essential academic requirements, the personal statement is your opportunity to illustrate whether your reasons for applying to this programme match what the programme will deliver.
UCL is regulated by the Office for Students.