With the rapid development of smart sensors, smartphones and social media, 'big' data is ubiquitous. This MSc teaches the foundations of GIScience, databases, spatial analysis, data mining and analytics to equip professionals with the tools and techniques to analyse, represent and model large and complex spatio-temporal datasets
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.
A minimum of an upper second-class UK Bachelor's degree in a relevant discipline (such as engineering, mathematics, computer science, environmental science, human or physical geography, geology, forestry, oceanography, or physics) or an overseas qualification of an equivalent standard. Applicants with relevant professional experience are also considered.
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
Students will be equipped with computational foundations and skills needed for big data analytics including visualisation, prediction, clustering and simulation with statistical and machine learning approaches, as well as retrieving and mining big (open) data, web services and cloud computing, web and mobile applications, all by practising with real case data and open software.
Students undertake modules to the value of 180 credits.
The programme consists of three core modules (45 credits), five optional modules (75 credits) and a dissertation/report (60 credits).
A Postgraduate Diploma, consisting of three core modules (45 credits) and five optional modules (75 credits), taken full-time over nine months is also offered.
Upon successful completion of 180 credits, you will be awarded a MSc in Spatio-Temporal Analytics and Big Data Mining. Upon successful completion of 120 credits, you will be awarded a PG Dip in Spatio-Temporal Analytics and Big Data Mining.
Please visit the Spatio-Temporal Analytics and Big Data Mining MSc programme page for further compulsory module details.
- Geospatial Science
- Spatial Analysis and Geocomputation
- Spatial-Temporal Data Analysis and Data Mining
Choose five options from the below list. Terms are indicated in brackets. Visit the Spatio-Temporal Analytics and Big Data Mining MSc programme page for further optional module details.
- Geospatial Programming (T1)
- Spatial Databases and Data Management (T1)
- Applied Machine Learning (T2)
- Mining Social and Geographic Datasets (T2)
- Sensors and Location (T2)
- Urban Simulation (T2)
- Web and Mobile GIS - Apps and Programming (T2)
Students may choose one elective module (any UCL Master's level module) in place of one optional module in Term 2, subject to approval from the programme director.
- Further information about these modules is available on the department website.
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.
All students undertake an independent research project which culminates in a dissertation of 15,000 words.
Teaching and learning
The programme is delivered through a combination of lectures, seminars, and laboratory practicals. Assessment is through examination, coursework, practicals, dissertation, and a poster presentation.
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Graduates from this programme are expected to find positions in consultancy, local government, public industry, and the information supply industry, as well as in continued research. Possible career paths could include: data scientist in the social media, finance, health, telecoms, retail or construction and planning industries; developer of spatial tools and specialised spatial software; researcher or entrepreneur.
Why study this degree at UCL?
As one of the world’s top universities, recently rated 8th in the QS World University Rankings 2020, UCL excels across the physical and engineering sciences, social sciences and humanities.
UCL Civil, Environmental & Geomatic Engineering is an energetic and exciting multidisciplinary department with a tradition of excellence in teaching and research. Students on the Spatio-Temporal Analytics and Big Data Mining MSc will be part of a vibrant, enthusiastic, and international research environment in which collaboration and free-ranging debate are strongly encouraged.
Students will benefit from the department’s excellent research and industry links, including attending our industrial and research seminar series, and carrying out a research project with one of our many industrial partners.
Department: Civil, Environmental & Geomatic Engineering
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 £80 for online applications and £105 for paper applications. Further information can be found at: www.ucl.ac.uk/prospective-students/graduate/taught/application.
Who can apply?
The programme is best suited to those in employment seeking continuing professional development or recent graduates who are looking for a career as a data scientist.
- All applicants
- 24 July 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 Spatio-temporal Analytics and Big Data Mining at graduate level
- why you want to study Spatio-temporal Analytics and Big Data Mining at UCL
- what particularly attracts you to this programme
- how your personal, academic and 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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