Prospective students

The MSc in Web Science and Big Data Analytics is a specialist programme. It covers fundamental aspects of web related technologies and big data analytics ranging from information search and retrieval, data mining and analytics, large-scale distributed and cloud computing, to e-commerce and their business economic models, and to the latest concepts of web 2.0 and social networks and the underlying networks science, with potential options in machine learning, artificial intelligence, finance, software engineering, and machine vision

The MSc Web Science and Big Data Analytics programme is intended for students with a general science and engineering background who wish to learn all aspects of quantitative web science and big data analytical skills.

We also offer the more Research orientated MRes Web Science and Big Data Analytics.

MSc Web Science and Big Data Analytics comprises 8 taught modules and a Dissertation. Of the taught modules, 3 are core modules, with a minimum of 3 optional modules and a combination of optional elective modules for the remainder.

Compulsory / Core Modules

  • COMP0123 - Complex Networks and Web (15 credits)
  • COMP0084 - Information Retrieval and Data Mining (15 credits)
  • COMP0124 - Multi-agent Artificial Intelligence (15 credits)
  • COMP0126 - MSc Thesis Project (60 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)
  • COMP0027 - Computer Graphics (15 credits)
  • COMP0047 - Data Analytics (15 credits)
  • COMP0039 - Entrepreneurship: Theory and Practice (15 credits)
  • COMP0080 - Graphical Models (15 credits)
  • COMP0137 - Machine Vision (15 credits)
  • COMP0086 - Probabilistic and Unsupervised Learning (15 credits)
  • COMP0087 - Statistical Natural Language Processing (15 credits)
  • XBKB0015 - Birkbeck College: Cloud Computing (15 credits)

Choose a minimum of 45 credits and a maximum of 60 credits from these optional modules.

All choices are subject to timetabling and resource constraints and the approval of the relevant Module Tutor (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)
  • COMP0078 - Supervised Learning (15 credits)

Choose a minimum of 15 credits and a maximum of 30 credits from the elective modules.

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


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.

A minimum of an upper second-class UK Bachelor's degree (or overseas equivalent) in a quantitative subject as computer science, engineering, mathematics, physics or a quantitative social science subject.

Applicants must be proficient in object-orientated and/or analytical programming, have strong communication skills, and an outstanding aptitude for quantitative analysis.

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.

UK/EU fees (FT):  £11,800 for 2017/18

Overseas fees (FT): £24,140 for 2017/18

UK/EU fees (FT):   £12,380 for 2018/19

UK/EU fees (PT):   N/A for 2018/19

Overseas fees (FT): £25,350 for 2017/18

Overseas fees (PT): N/A for 2018/19

The Department of Computer Science is offering Excellence Scholarships to our taught postgraduate students. To check your eligibility and to apply, see the Computer Science Excellence Scholarship application form.

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:

UK/EUOverseas
Full-time*Part-timeFull-time*Part-time
£2000£1000£2000£1000
*where part-time is an available mode of study

MSc Web Science's unique combination of technical skills makes graduates well equipped to proceed to scientific research or the ideal choice for the best employers in Internet related industries and the areas requiring large-scale data analytical skills.

Top graduate destinations include:

Top graduate roles include:

Top further study destinations:

  • Microsoft
  • SAS
  • Google
  • Big Data Architect
  • Senior Data Analyst
  • Technology Consultant
  • University of Cambridge
  • UCL

Average starting salary £31,200 (Graduate Surveys, January 2015).

Programme Administrator

Samantha Bottomley

Office: 5.22, Malet Place Engineering Building

Telephone: +44 (0) 20 7679 0328

Email: advancedmsc-admissions@cs.ucl.ac.uk

Click here for more information

To apply now click here.

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.

Deadline 15th June 2018.