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 (2022/23)
£16,500
£8,250
Overseas tuition fees (2022/23)
£35,100
£17,550
Duration
1 calendar year
2 calendar years
Programme starts
September 2022
Applications accepted
All applicants: 18 Oct 2021 – 31 Mar 2022

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.

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: Standard

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. International Preparation Courses

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.

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.

Accreditation

This programme has been accredited by the Royal Statistical Society (RSS). The current period of accreditation covers students who first enrol between September 2017 and September 2022. Students will be eligible for e-Student membership of the RSS, with the potential to progress along the professional pathway of RSS membership to Graduate Statistician and Chartered Statistician status.

Teaching and learning

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.

Modules

Full-time

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.

Part-time

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 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 is subject to change.

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 & Wellbeing team.

Online - Open day

Graduate Open Events: Applying for Graduate Study at UCL

Fees and funding

Fees for this course

UK students International students
Fee description Full-time Part-time
Tuition fees (2022/23) £16,500 £8,250
Tuition fees (2022/23) £35,100 £17,550

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.

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 40 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 in any application cycle.

We recommend that you submit your application as soon as possible. The programme may remain open if places are still available after 31 March 2022 and will be closed as soon as it is full or by 30 June 2022.

UCL is regulated by the Office for Students.

This page was last updated 28 Sep 2021