Data Science brings together computational and statistical skills for data-driven problem solving. This rapidly expanding area includes machine learning, deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence. The MSc provides a principled understanding of the computational and statistical underpinnings of current and emerging methods.
Data Science is an emerging, and increasingly important academic discipline. The field is supported by vocational and commercial opportunities, in particular in London, which has a growing, vibrant community of data scientists and machine learning researchers.
MSc Data Science and Machine Learning comprises 8 taught modules and a Project. Of the taught modules, 3 are core modules, with either 2 optional and 3 elective modules or 3 optional and 2 elective modules.
Compulsory / Core Modules
- COMP0081 - Applied Machine Learning (15 credits)
- COMP0088 - Introduction to Machine Learning (15 credits)
- STAT0032 - Introduction to Statistical Science (15 credits)
- COMP0091 - Project (60 credits)
All modules in this group are compulsory.
- COMP0089 - Advanced Deep Learning and Reinforcement Learning (15 credits)
- COMP0084 - Information Retrieval and Data Mining (15 credits)
- COMP0090 - Introduction to Deep Learning (15 credits)
- COMP0137 - Machine Vision (15 credits)
- COMP0124 - Multi-agent Artificial Intelligence (15 credits)
- COMP0087 - Statistical Natural Language Processing (15 credits)
- XBKB0015 - Birkbeck College: Cloud Computing (15 credits)
Choose a minimum of 30 credits and a maximum of 45 credits from these optional modules.
All choices are subject to space and timetabling constraints.
- COMP0053 - Affective Computing and Human-Robot Interaction (15 credits)
- COMP0082 - Bioinformatics (15 credits)
- COMP0118 - Computational Modelling for Biomedical Imaging (15 credits)
- COMP0080 - Graphical Models (15 credits)
- COMP0078 - Supervised Learning (15 credits)
- STAT0031 - Applied Bayesian Methods (15 credits)
- STAT0011 - Decision and Risk (15 credits)
- STAT0010 - Forecasting (15 credits)
- STAT0029 - Statistical Design of Investigations (15 credits)
Choose a minimum of 30 credits and a maximum of 45 credits from these Elective modules.
All choices are subject to timetabling constraints and the approval of the relevant Module Tutor (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 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 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 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.
A minimum of an upper second-class Bachelor's degree (or equivalent overseas qualification) in a quantitative discipline* (such as mathematics, computer science, engineering, physics or statistics) from a UK university or an overseas, qualification of an equivalent standard.
*Knowledge of mathematical methods including linear algebra and calculus at first-year university level is required.
Depending on the modules selected, students undertake assignments that contain programming elements and prior experience in a high-level programming language (R/matlab/python) is useful. 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: 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.
2019/20 Tuition Fees
UK/EU Fees (FT):
£13340 for 2019/20
UK/EU Fees (PT):
N/A for 2019/20
Overseas Fees (FT):
£28410 for 2019/20
Overseas Fees (PT):
N/A for 2019/20
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:
|*only applicable where part-time is an available mode of study|
The Department's graduates are particularly valued as a result of the our international reputation, strong links with industry, and ideal location close to the City of London.
Data science related graduate destinations:
Data science relates graduate roles:
Top further study destinations:
Average starting salary £34,120 (all data from Graduate Surveys, January 2015)
Data Science professionals will be highly sought after as the integration of statistical and computational analytical tools becomes increasingly essential in all kinds of organisations and enterprises. A solid understanding of fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should go along with statistical expertise at graduate level. Data scientists will have a broad background so they will be able to adapt themselves to rapidly evolving challenges.
To apply now click here.
This MSc receives many more applications than it has places available and the admissions process is competitive. It may therefore take longer than the Admissions stated 6 weeks for a decision to be made and communicated. Applicants are advised to apply as early as possible due to the competition for places.
Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
Deadline 14th June 2019.