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Spatio-temporal Analytics and Big Data Mining MSc

With the rapid development of smart sensors, smartphones and social media, "big" data is ubiquitous. This new MSc teaches the foundations of GIScience, database, 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.

Key information

Programme starts

September 2017

Modes and duration

Full time: 1 year

Tuition fees (2017/18)

£11,800 (FT) N/A (PT)
£24,610 (FT) N/A (PT)

Application dates

All applicants
Open: 3 October 2016
Close: 28 July 2017

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 Current Students website.

Optional qualifications: This degree is also available as a PG Diploma with fees set accordingly.
Location: London, Bloomsbury

Entry requirements

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.

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.

International applicants can find out the equivalent qualification for their country by selecting from the list below.

Select your country:

Degree information

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, by practising with real case data and open software.

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 dissertation/report (60 credits).

A Postgraduate Diploma, four core modules (60 credits), two optional modules (60 credits), full-time nine months is offered.

Core modules

  • GIS Principles and Technology
  • Principles of Spatial Analysis
  • Spatial Databases and Data Management
  • Spatio-temporal Analysis and Data Mining

Choose four options from the following:

  • Introductory Programming (requires Applied Machine Learning option)
  • Complex Networks and Web
  • Representation, Structures and Algorithms
  • Mapping Science
  • Supervised Learning (requires Applied Machine Learning)
  • Web Mobile GIS
  • Information Retrieval & Data Mining (requires Introductory Programming)
  • Geographic Information System Design
  • Applied Machine Learning (requires Introductory Programming, and Supervised Learning)


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 poster presentation.

Further information on modules and degree structure is available on the department website: Spatio-temporal Analytics and Big Data Mining MSc


Scholarships relevant to this department are displayed below.

Commonwealth Shared Scholarship Scheme (CSSS) - NOW CLOSED FOR 2016/17

Full fees, flights, stipend, and other allowances (1 year)
Overseas students
Based on both academic merit and financial need

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.


Graduates will be equipped with essential principles and technical skills in managing, modelling, spatial and spatial-temporal analysis, visualising and simulating "big" spatio-temporal data, with emphasis on real development skills including: Java, JavaScript, Python and R. Business Intelligence (BI) skills will also be taught via practical case studies and close collaborations with leading industrial companies and institutions. All these skills are highly valued in big data analysis.

Why study this degree at UCL?

As one of the world’s top universities, UCL excels across the physical and engineering sciences, social sciences and humanities.

Spanning two UCL faculties, this interdisciplinary programme exploits the complementary research interests and teaching programmes of three departments (Civil, Environmental & Geomatic Engineering, Computer Science, and Geography).

Students on the Spatio-Temporal Analytics and Big Data Mining programme will be part of a vibrant, enthusiastic, and international research environment in which collaboration and free-ranging debate are strongly encouraged. This is supported by weekly research seminars and industrial seminars from top employers in the field.

Department: Civil, Environmental & Geomatic Engineering

Student / staff numbers › 107 staff including 48 postdocs › 154 taught students › 119 research students

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Civil, Environmental & Geomatic Engineering
78% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.

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 best suited to those in employment seeking continuing professional development or recent graduates who are looking for a career as a data scientist.

Application deadlines

All applicants
28 July 2017

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.

Contact information

Page last modified on 20 October 2016 at 12:07 by UCL Publications & Marketing Services. Please contact us for content updates.


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