Web Science and Big Data Analytics MRes
The MRes covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and is an ideal preparation for roles with some of the best employers in internet-related industries and areas requiring big data analytical skills.
Modes and duration
Tuition fees (2018/19)
- £12,380 (FT)
- £25,880 (FT)
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 Current 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 UK Bachelor's degree in a highly quantitative subject, or an overseas qualification of an equivalent standard. Students should also have some experience with a programming language such as Java or python.
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
Students will gain a detailed knowledge and understanding of the fundamental principles and technological components of the World Wide Web, learning not only the latest web search and information retrieval technologies and their underlying computational and statistical methods, but also studying essential large-scale data analytics to extract insights and patterns from vast amounts of unstructured data.
Students undertake modules to the value of 180 credits.
The programme consists of two core modules (30 credits), either four optional modules (60 credits) or three optional and one elective module, and the research dissertation (90 credits).
- Investigating Research (15 credits)
- Researcher Professional Development (15 credits)
Students must choose a minimum of 45 and a maximum of 60 credits of optional modules. Students may also choose up to 15 credits from electives.
- Affective Computing and Human-Robot Interaction (15 credits)
- Complex Networks and Web (15 credits)
- Computer Graphics (15 credits)
- Graphical Models (15 credits)
- Information Retrieval and Data Mining (15 credits)
- Machine Vision (15 credits)
- Probabilistic and Unsupervised Learning (15 credits)
- Statistical Natural Language Processing (15 credits)
- Web Economics (15 credits)
Please note: the availability and delivery of modules may vary, based on your selected options.
A list of acceptable elective modules is available on the Departmental page.
All students undertake an independent research project which culminates in a substantial dissertation.
Teaching and learning
The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with help from demonstrators. For the research project, each student liaises with their academic or industrial supervisor to choose a study area of mutual interest. Student performance is assessed by unseen written examinations, coursework and the research dissertation.
Applicants considering a career in the media industry can get a head start by applying for the Channel 4 Graduate Data Scientist role, a two-year fixed term contract, completing the MRes in year one, then working full-time for Channel 4 in year two.
The Channel 4 closing date is 24:00, 15 March 2017. Applications should be submitted to both Channel 4 and UCL.
Scholarships relevant to this department are displayed below.
- Deadline is 11 May 2018
- £15,000 (1 year)
- Based on both academic merit and financial need
- Deadline: 30 June 2018
- £4,000 (1 year)
- UK, EU
- 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.
Graduates from UCL are keenly sought after by the world's leading organisations, and many progress in their careers to secure senior and influential positions. Graduates of our Web Science and Big Data Analytics programmes are expected to develop careers in scientific research, the internet-based industry and other professional areas that require big data analytics skills.
Recent career destinations for this degree
- Software Developer, British Film Institute (BFI)
- Software Developer, Geneity
The MRes has a unique industry connection as almost all our lecturers have industry experience. Through long-term collaborations with big players in the field such as Google, Microsoft and BT, their research is driven by the fundamental technical challenges faced by the industry. Throughout the degree, our students have the chance to interact with our industry collaborators and previous students, and have placement opportunities to address specific technical problems faced by the industry.
Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.
Why study this degree at UCL?
UCL Computer Science is recognised as a world leader in teaching and research. 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).
Our Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries, from entertainment to finance.
We take an experimental approach to our subject, enjoy the challenge and opportunity of entrepreneurial partnerships and place a high value on our extensive range of industrial collaborations.
Department: Computer Science
Student / staff numbers
› 200 staff
including 130 postdocs
› 470 taught students
› 220 research students
Staff/student numbers information correct as of 1 August 2017.
Research Excellence Framework (REF)
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
The following REF score was awarded to the department: Computer Science
96% rated 4* (world-leading) or 3* (internationally excellent)
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.
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.
Who can apply?
The MRes programme is highly flexible and specific, and is tailored to students' individual needs. It is intended for students who have a background in internet-based businesses and who have a specific technical question in mind for a substantial research project.
- All applicants
- 15 June 2018
For more information see our Applications page.Apply now
What are we looking for?
When we assess your application we would like to learn:
- why you want to study Web Science and Big Data Analytics at graduate level
- why you want to study Web Science and Big Data Analytics at UCL
- what particularly attracts you to this programme
- how your academic and professional background meets the demands of this programme
- what programming experience you have
- 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.