The Spatial Data Science and Visualisation MSc teaches cutting-edge data analysis, mining, modelling and visualisation techniques for spatial systems. Students carry out their own research project, supported by academics, researchers and other students in one of the most exciting, interdisciplinary research teams in the field. The programme takes place within The Bartlett, UCL's Faculty of the Built Environment.
Modes and duration
Full-time students study for 37.5 hours per week during term time. Typically, lectures and seminars occur on two days per week. Flexible students normally attend half this amount.
Tuition fees (2019/20)
- £14,610 (FT) £7,190 (PT)
- £26,660 (FT) £13,340 (PT)
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 Students website.
A minimum of a second-class Bachelor's degree in a relevant discipline from a UK university, or an overseas qualification of an equivalent standard.
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
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
The programme consists of four core modules (60 credits), a group mini-project (30 credits), two elective modules (30 credits), and a dissertation (60 credits).
The core modules focus on technical skills, leading to applications in mapping, visualising and analysing spatial data.
- Data Science for Spatial Systems
- Geographic Information Systems and Science
- Introduction to Programming for Spatial Analysts
- Quantitative Methods
- Group Mini Project: Digital Visualisation
Students select two elective modules from a wide range available at UCL, subject to approval.
All students submit a dissertation of 10-12,000 words.
Teaching and learning
The programme is delivered through a combination of lectures, seminars, tutorials and practical-based workshops and classes. The interlinked laboratory research-based mini project with data collection focuses on ‘remote data mining’ rather than fieldwork in the traditional planning/geographical/architectural sense. Assessment is through group and individual projects and the dissertation.
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Recent graduates of our related Spatial Data Science and Visualisation MRes have gone on to work as developers, in spatial analysis, and a number have continued to PhDs. Through our PhD partners, Knowledge Transfer Partnerships and substantial outreach, graduates will be able to take advantage of CASA's links to the world outside academia.
The Spatial Data Science and Visualisation MSc provides a unique skill set in computation mapping, visualisation and spatial research. Research-led skills are increasingly a key element in our understanding of complex spatial functions, particularly as vast amounts of previously unused data are becoming available either from changes in accessibility regulation or more widely as a result of new mass data collection methodologies.
Why study this degree at UCL?
The Centre for Advanced Spatial Analysis (CASA) is a research centre specialising in computational and mathematical approaches, with cutting-edge research in GIS, urban simulation, mapping, data visualisation, and 3D environments in cities and space.
Students on this programme will be exposed to a range of programming languages (Java/Processing, R, Python and MySQL), 3D visualisation packages, and be given a substantive grounding in GIS, programming structure, mathematical methods and data design.
The combination of skills involved in this programme is unique – graduates will be able to lead institutions and companies in new directions and be involved in changing cultures across the sector.
Department: Centre for Advanced Spatial Analysis
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.
There is an application processing fee for this programme of £75 for online applications and £100 for paper applications. Further information can be found at: www.ucl.ac.uk/prospective-students/graduate/taught/application.
Who can apply?
The programme is ideal for people interested in creating visualisations, maps, analyses and models to understand spatial systems. Our students come from both humanities and social and physical science backgrounds, from areas including geography, architecture, planning, software development, philosophy and landscape architecture.
The programme has computational and technical elements, but we do not require computing qualifications for entry; just enthusiasm and the desire to learn.
- All applicants
- 26 July 2019
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 Spatial Data Science and Visualisation at graduate level
- why you want to study Spatial Data Science and Visualisation 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.