Prospective students

**Please note** This programme will not run from 2019/20. Information valid for 2018/19 only.

Our MRes in Web Science and Big Data Analytics is highly flexible and specific, and is tailored to suit students' individual needs. It is problem-based learning and students will be encouraged to bring up their own technical problems for research, though not required. They will be able to choose their optional modules from a wide range of specialised options, including programming and analytical modules, and will research and write a dissertation based on a research project. It is a cost-effective way of addressing a specific technical problem the industry is facing. More specifically:

  • students will start with a specific problem and choose modules based on the needed knowledge,
  • then be liaised with their academic or industrial supervisor to choose a study area of mutual interest, and
  • research and write a dissertation based on a 10-month research project.

It is intended for students who have a background in the Internet-based businesses (though not essential) and who have a specific technical question in mind for a substantial research project. We also offer the more Teaching orientated MSc Web Science and Big Data Analytics.

In our MRes Web Science and Big Data Analytics, students will gain a detailed knowledge and understanding of the fundamental principles and technological components of the World Wide Web and essential computational and statistical skills; they will not only learn the state of the art (Web) search and information retrieval technologies and their underlying computational and statistical methods, but also study essential large-scale data analytics to discover and extract insights, patterns, and useful knowledge from vast amounts of unstructured data produced daily by (Web) users and systems in various fields.

Students undertake courses to the value of 180 credits. The programme consists of three core modules (45 credits), the research dissertation (90 credits), and either three optional modules or two optional and one elective module.

Compulsory / Core Modules

  • COMP0084 - Information Retrieval and Data Mining (15 credits)
  • EDUC0001 - Investigating Research (15 credits)
  • EDUC0002 - Researcher Professional Development (15 credits)
  • COMP0125 - MRes Dissertation (90 credits)

All modules in this group are compulsory.

Optional Modules

  • COMP0053 - Affective Computing and Human-Robot Interaction (15 credits)
  • COMP0081 - Applied Machine Learning (15 credits)
  • COMP0123 - Complex Networks and Web (15 credits)
  • COMP0027 - Computer Graphics (15 credits)
  • COMP0039 - Entrepreneurship: Theory and Practice (15 credits)
  • COMP0080 - Graphical Models (15 credits)
  • COMP0137 - Machine Vision (15 credits)
  • COMP0124 - Multi-agent Artificial Intelligence (15 credits)
  • COMP0086 - Probabilistic and Unsupervised Learning (15 credits)
  • COMP0087 - Statistical Natural Language Processing (15 credits)

Choose a minimum of 30 credits and a maximum of 45 credits from these optional modules.

All choices are subject to timetabling constraints and the approval of the relevant Module Tutors (i.e. to ensure any prerequisites are satisfied) and the Programme Director.

Elective Modules

  • COMP0070 - Algorithmics (15 credits)
  • COMP0040 - Financial Data and Statistics (15 credits)
  • COMP0090 - Introduction to Deep Learning (15 credits)
  • COMP0066 - Introductory Programming (15 credits)
  • COMP0043 - Numerical Methods for Finance (15 credits)
  • COMP0045 - Probability Theory and Stochastic Processes (15 credits)
  • COMP0113 - Virtual Environments (15 credits)

Choose a minimum of 0 credits and a maximum of 15 credits from these elective modules.

All choices are subject to timetabling constraints and the approval of the relevant Module Tutors (i.e. to ensure any prerequisites are satisfied) and the Programme Director.

Syllabus content for all postgraduate modules can we found in the Department of Computer Science 2018/19 online syllabus pages.

Programme diet (modules available to you)

Your programme has a set curriculum (also called a diet) which prescribes in what combinations modules can be taken, any restrictions on doing so, and how much credit can and must be taken. The programme information pages show which modules form part of each programme, with links to descriptions and module syllabus information. Modules within a programme can be core, optional, or elective, which reflects whether they must be taken or are optionally taken.

Core modules

Core modules are fundamental to your programme’s core curriculum and are mandatory. You will automatically be registered on your programme's core modules, so will not have to select them. You are guaranteed a place on modules that are core for your programme. There will be no timetable clashes between core modules within a programme.

Optional modules

Optional modules are usually closely related to the programme's core curriculum and you will be able to choose which to take; choices are usually made from within specific groups (for example, choose two optional modules from one group and three from another, etc.) You are not guaranteed a place on optional modules as space is strictly limited. We allocate places on a first come, first serve basis, with preference given to Computer Science students over those of other departments. Bear in mind that some modules have prerequisites that must be met in order to be eligible for a place (see the module syllabus for information.)

Elective modules

Elective modules are usually not specifically related to the programme's curriculum. There is no guarantee of being accepted onto an elective module; they are core and/ or optional on other programme diets, so students on those programmes will be given priority. As with optional modules, some electives have prerequisites that must be met.

Deciding which modules to select

The programme information pages show which modules form part of each programme, with links to detailed module syllabus information and reading lists. You may be able to virtually audit lectures for some modules to get a sense of how the module is delivered. You can look up the timetable for each module via the common timetable to get a sense of the timetable that would eventuate from your module choices, which is an important consideration when making your final choices; you should aim to achieve a timetable that is feasible and will not stretch you too thinly.

Bear in mind that places on optional and elective modules are not guaranteed, so you might not always be able to take all your first choices. In that case, it is a good idea to have a second preference in mind.

If you need guidance with choosing which modules to select then please contact your Programme Director or the Departmental Tutor.

The MRes is designed for people who have a first or upper second class honours degree (or equivalent) in a highly quantitative subject such as Computer Science, Mathematics, Electrical Engineering or the Physical Sciences.

Industrial experience may compensate for lesser degrees or lack of technical qualification.

Successful candidates will have proven experience with programming languages such as C/ C++, Java or Python and should have a strong foundation in mathematics including vector and matrix algebra, calculus, and probability and statistics.

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.

International students

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.

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

Tuition Fee Deposit

This programme requires that applicants firmly accepting their offer pay a deposit. This allows UCL to effectively plan student numbers, as students are more demonstrably committed towards commencing their studies with us.

For full details about the UCL tuition fee deposit, please see the central UCL pages.

Tuition fee deposits within the Department of Computer Science are currently listed as:

*only applicable where part-time is an available mode of study

Top graduate destinations include:

Top graduate roles include:

Top further study destinations:

  • Accenture
  • Citigroup
  • Google
  • IBM
  • Implementation specialist
  • Java Developer
  • Software Engineer
  • Technology Consultant
  • University of Cambridge
  • UCL
  • MIT

Average starting salary £31,200 (all data from Graduate Surveys, January 2014)

Programme Administrator

Samantha Bottomley

Office: 5.22, Malet Place Engineering Building

Telephone: +44 (0) 20 7679 0328


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