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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 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

Key information

Programme starts

September 2021

Modes and duration

Full time: 1 year

Application dates

All applicants
Open: 9 November 2020
Close: 31 May 2021
Notification
Applications may close earlier if all places on the programme are filled.

Tuition fees (2021/22)

UK:
£16,000 (FT)
Overseas:
£31,200 (FT)


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: ucl.ac.uk/students/fees.

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

Pre-Master's and Pre-sessional English

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level. International Preparation Courses

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:

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 five core modules (75 credits), three optional modules (45 credits) and a dissertation/report (60 credits).

A Postgraduate Diploma, consisting of five core modules (75 credits) and three optional modules (45 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 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.

Compulsory modules

  • Geospatial Science
  • Geospatial Programming
  • Spatial Analysis and Geocomputation
  • Spatial-Temporal Data Analysis and Data Mining
  • Machine Learning for Data Science


Optional modules

Choose five options from the below list. Terms are indicated in brackets.

  • Spatial Databases and Data Management (T1)
  • 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.

Dissertation/report

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.

Additional costs

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk. Further information can also be obtained from the UCL Student Support & Wellbeing team.

Funding

Scholarships relevant to this department are displayed below.

CEGE MSc/MRes Bursary

Note:
Deadline: 11.59pm, 31 July 2021  
Value:
Full or partial fee waiver (1yr)
Eligibility:
UK, EU, Overseas
Criteria:
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.

Careers

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.

Employability

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, 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

Applications

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.

This programme requires two references. Further information regarding references can be found in our How to apply section.

There is an application processing fee for this programme of £90 for online applications and £115 for paper applications. Further information can be found at: 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.

Application deadlines

All applicants
31 May 2021

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.

UCL is regulated by the Office for Students.

Page last modified on 28 August 2021