What the Spatial Data Science and Visualisation degree?

Our masters degrees in Spatial Data Science and Visualisation teach cutting-edge data analysis, mining, modelling and visualisation techniques for spatial systems. Students carry out their own research project, supported by academics, researchers and students in one of the most exciting, interdisciplinary research teams in the field, within The Bartlett – UCL’s multi-disciplinary Faculty of the Built Environment.

Graduates from our programme will have been exposed to a range of programming languages (Processing, R, Python and MySql), 3D visualisation packages, and a substantive grounding in GIS, programming structure, mathematical methods and data design.

This combination of skills is unique – graduates from this programme will be leading the institutions and companies in new directions and changing cultures across the sector.

What is the difference between the MRes and the MSc?

An MRes is a Master of Research. MRes degrees are less focussed on taught courses than a traditional MSc or MA, and instead allow students to carry out a research project that acts as a demonstration of independent thought, creativity and innovation for those wanting to study further, or as a calling card for those moving into industry.

The MSc has the same taught components as the MRes, with two additional elective modules chosen from the Agent Based Modelling for Spatial Systems and modules led by the Bartlett School of Architecture and Geomatics. The research project (dissertation module) is smaller in order to accommodate this taught material.

Course Structure

MRes: The programme consists of four core modules (60 credits), a group project (30 credits) and a research dissertation (90 credits).

MSc: The programme consists of four core modules (60 credits), two elective modules, a group project (30 credits) and a dissertation (60 credits).

Core Modules (MSc and MRes)

Elective Modules (MSc Only) – take two of the following

  • Agent Based Modelling for Spatial Systems (15 credits)
  • Computational Analysis (15 credits)
  • Spatio-temporal analysis and data mining (15 credits)
  • Space Syntax Methodology and Analytical Design (15 credits)

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.