There is ever-growing national and international demand for qualified professionals and scientists who have expertise in social and geographic data science. UCL's Social and Geographic Data Science MSc provides rigorous scientific and vocational training for the next generation of data scientists, with a unique expertise at the interface of social sciences, computing and statistics.
Modes and duration
Tuition fees (2020/21)
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.
Normally a minimum of an upper 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: 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
The programme combines a rigorous treatment of underlying theory for, and methods of implementing and exploiting, spatial analysis,decision support, and data science. Optional modules provide the opportunity to develop expertise in modelling and analysis in one or more areas of social and environmental science with social and policy dimensions.
Students undertake modules to the value of 180 credits.
The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research dissertation (60 credits).
A Postgraduate Diploma (120 credits, full-time nine months, part-time two years) is offered.
A Postgraduate Certificate (60 credits, full-time 12 weeks, part-time one year) is offered.
Upon successful completion of 180 credits, you will be awarded a MSc in Social and Geographic Data Science. Upon successful completion of 120 credits, you will be awarded a PG Dip in Social and Geographic Data Science. Upon successful completion of 60 credits, you will be awarded a PG Cert in Social and Geographic Data Science.
Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change.
- Geographic Information Systems and Science
- Principles of Spatial Analysis
- Introduction to Data Science
- Data, Politics and Society
- Advanced Topics in Social Data Science
- Mining Social and Geographic Datasets
- Introduction to Citizen Science and Scientific Crowdsourcing
- Cartography and Data Visualisation
- 1 x 15 credit module may be taken from other Geography MSc programmes or from outside Geography, subject to availability and approval from the MSc programme convenor
All MSc students undertake an independent research project which culminates in a dissertation of approximately 12,000 words and a poster presentation.
Teaching and learning
The programme is delivered through a combination of lectures, seminars, tutorials and laboratory and computer-based practical classes. Assessment is through independent project work, practical-based and written coursework, 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.
There is a national and international need for scientists with skills in data science based analysis and decision support complemented by a deep knowledge of domain-specific models and analytical methods that can be brought to bear on environmental issues and their social consequences. The MSc provides an ideal foundation for PhD research, or for prospective employment within research organisations, consultancies, government departments and a wide range of industries.
Why study this degree at UCL?
The Social and Geographic Data Science MSc is run by UCL Geography, which enjoys an outstanding international reputation for its research and teaching. The programme brings together the department's strong expertise in spatial science and social and environmental modelling.
Students enter a vibrant, enthusiastic, and international research environment in which collaboration and free-ranging debate are strongly encouraged. UCL's location, in central London, provides easy access to many key intellectual venues and resources, such as the British Library.
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 programme is particularly suitable for students with an undergraduate degree in geography, Earth or environmental science, geomatics, oceanography, or another relevant discipline. Applicants with relevant professional experience in geographic information systems, data science, spatial analysis or decision support will also be considered.
- All applicants
- 28 August 2020
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 Social and Geographic Data Science
- why you want to study Social and Geographic Data Science at UCL
- what particularly attracts you to this programme
- how your academic and/or professional background meets the demands of a challenging academic environment
- 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.
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