Data Science MSc

London, Bloomsbury

Data science brings together computational and statistical skills for data-driven problem solving. This programme will equip students with the analytical tools to design sophisticated technical solutions using modern computational methods and with an emphasis on rigorous statistical thinking.

UK students International students
Study mode
UK tuition fees (2024/25)
£19,300
£9,650
Overseas tuition fees (2024/25)
£41,000
£20,500
Duration
1 calendar year
2 calendar years
Programme starts
September 2024
Applications accepted
Applicants who require a visa: 16 Oct 2023 – 05 Apr 2024
Applications close at 5pm UK time

Applications open

Applicants who do not require a visa: 16 Oct 2023 – 30 Aug 2024
Applications close at 5pm UK time

Applications open

Entry requirements

A minimum of an upper second-class Bachelor's degree in a quantitative discipline from a UK university or an overseas qualification of an equivalent standard. Knowledge of mathematical methods and linear algebra at university level is expected, along with evidence of familiarity with introductory probability, statistics and computer programming. Prior experience in a high-level programming language (e.g. R/matlab/python) is a requirement. Relevant professional experience will also be taken into consideration.

The English language level for this programme is: Level 1

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

Equivalent qualifications

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. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical computing and modelling. Students will take one compulsory module and up to two additional modules in computer science, with the remaining modules (including the research project) taken mainly from within statistical science.

Who this course is for

The programme is accessible to students with undergraduate degrees in a related quantitative discipline (such as mathematics, statistics, economics, actuarial science), who wish to gain advanced training in statistical analysis and computation to enable them to enter specialist employment or academic research. The modules in this MSc programme require computer programming in both R and Python, and most modules in computer science assume familiarity with Python coding.

What this course will give you

UCL Statistical Science has a broad range of research interests, but has particular strengths in the area of computational statistics and in the interface between statistics and computer science.

UCL's Centre for Computational Statistics and Machine Learning, in which many members of the department are active, has a programme of seminars, masterclasses and other events. 

UCL is one of the founding members of the Alan Turing Institute, and both UCL Statistical Science and UCL Computer Science will be playing major roles in this exciting new development which will make London a major focus for big data research.

The foundation of your career

Data science professionals are likely to be increasingly sought after as the integration of statistical and computational analytical tools becomes essential in all kinds of organisations and enterprises. A thorough understanding of the fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should be accompanied by statistical expertise at graduate level. Data scientists need a broad background knowledge so that they will be able to adapt to rapidly evolving challenges.

Employability

Graduates from UCL Statistical Science typically enter professional employment across a broad range of industry sectors or pursue further academic study.

Areas of employment include IT, Technology and Telecoms, and Accountancy and Financial Services with graduates securing positions with a range of employers including Deloitte and Huawei.

Networking

The Department offers world-class expertise along with strong links to practitioners, and its position withing UCL provides with breadth of knowledge (for example the UCL Institute for Mathematical and Statistical Sciences, the UCL Centre for Computational Statistics and Machine Learning and the Alan Turing Institute). Staff members also collaborate directly with hospitals, power companies, government regulators, and the financial sector. Consequently, postgraduate students have opportunities to engage with external institutions. There is the possibility of external organisations delivering technical lectures and seminars while the MS research project list usually includes some collaborative projects with pharmaceutical companies and other industrial partners.

Accreditation

This MSc programme was accredited by the Royal Statistical Society up until 2023/24. The department is in the process of applying to the Royal Statistical Society to renew accreditation.

Teaching and learning

The primary method of communicating information and stimulating interest is through lectures, which provide you with a formal knowledge base from which your understanding can be developed. Understanding of lecture material is reinforced by problem classes, computer workshops and group tutorials, as well as by self-study. Peer-assisted learning, discussion with other students and individual discussion with staff also support the learning process.

Whereas lectures provide the primary vehicle for accumulating a knowledge base, your intellectual, academic and research skills will mainly be developed outside of the lecture theatre, for example, by tackling and discussing problems set on a regular (usually weekly) basis. Some coursework requires you to develop your thinking beyond rote learning, and to link ideas between different modules. You will be encouraged to reason openly through discussion of set problems in tutorials. For some modules, workshops allow you to work on problems individually or in groups, with teaching staff / assistants present to give help. Teaching staff also hold regular "office hours" during which you are welcome to come and ask questions about the material and obtain individual (one-to-one) assistance and feedback.

Practical and transferable skills are developed by the provision of opportunities for hands-on experience through regular workshops and projects. Data analysis demonstrations and exercises are an essential component of the core modules and much of the tuition for statistical computing takes place in computer workshops, which will allow you to learn through active participation. Additional workshops running during the teaching terms provide preparation for the summer research project and cover the communication of statistics, for example, the presentation of statistical graphs and tables. Project supervisors will provide guidance on how to manage an extended task effectively and you are encouraged to monitor your own working practice using a self-assessment questionnaire, as well as to monitor your own progress by self-marking of non-assessed coursework.

All summative assessment is organised at modular level during the academic year in which the module is taken. Most Statistical Science and Computer Science modules employ a combination of end-of-year written examination and coursework to assess your subject-specific knowledge and academic skills, although some modules are entirely coursework based. Data analysis project work further assesses your intellectual, academic and research skills by means of word-processed written reports and, in the case of the summer research project, an oral presentation.

Coursework is designed to encourage you to develop your knowledge and skills as each module proceeds. Although not all coursework contributes towards formal assessment, it will provide you with the opportunity to demonstrate your intellectual and practical skills in written responses to problem sheets and in oral responses during tutorials, with feedback mainly presented through tutorials / problem classes / workshops, and on an individual basis on request.

On average it is expected that a student spends 150 hours studying for each 15-credit module. This includes teaching time, private study and coursework. Modules are usually taught in weekly two-hour sessions over 10 weeks each term.

Modules

The core methodology is delivered through a foundation module (to revise basic concepts in probability and statistics) and further compulsory modules, and illustrated with a variety of applications. Programming techniques are introduced within the core modules in order to allow students to code their own statistical methods. Students may then place particular emphasis on their application areas of interest by suitable choice of optional modules.

The research project is a consolidation of the MSc’s taught component. Students will normally analyse and interpret data from a real, complex problem, offering the chance to produce viable solutions. Project topics can be selected from a departmental list, or students can make their own suggestions. The list usually includes some collaborative projects available with industrial partners.

The programme is also offered on a part-time basis over two years. The taught modules are split between the first and second years, but within each year the classes for a particular module are the same ones attended by full-time students (i.e. special teaching times are not offered for the part-time programme).

The foundation module is taken at the beginning of the first year. It is recommended that students also take the compulsory module Introduction to Statistical Data Science (STAT0032) in the first year, and module prerequisites need to be fulfilled, but otherwise there is some flexibility in the order that the remaining taught modules can be studied. Part-time students submit their project at the end of the second year. It is possible to arrange with the project supervisor to start to work on the project earlier than full-time students, but part time students are not entitled to a higher amount of supervision overall.

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 are subject to change. Modules that are in use for the current academic year are linked for further information. Where no link is present, further information is not yet available.

Students undertake modules to the value of 180 credits. Upon successful completion of 180 credits, you will be awarded an MSc in Data Science.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk. Further information can also be obtained from the UCL Student Support and Wellbeing team.

Fees and funding

Fees for this course

UK students International students
Fee description Full-time Part-time
Tuition fees (2024/25) £19,300 £9,650
Tuition fees (2024/25) £41,000 £20,500

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees.

Additional costs

There are no programme-specific costs.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs.

Funding your studies

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.

Brown Family Bursary

Deadline: 20 June 2024
Value: £15,000 (1 year)
Criteria Based on both academic merit and financial need
Eligibility: UK

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 £90 for online applications and £115 for paper applications. Further information can be found at Application fees.

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

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.

Please note that the admissions process is expected to be highly competitive - in the previous cycle we received over 20 applications per available place. Reaching the standard entry requirements therefore provides no guarantee that any offer will be made.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Choose your programme

Please read the Application Guidance before proceeding with your application.

Year of entry: 2024-2025

UCL is regulated by the Office for Students.