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MSc Urban Spatial Science Degree Apprenticeship

  • DeadlineStudy Details: 3 academic years

Masters Degree Description

The Urban Spatial Science Degree Apprenticeship MSc equips students with a multidisciplinary, critical lens for analysing and shaping global urban resilience and sustainability. You’ll gain expertise in data analytics, machine learning (ML), remote sensing, and reproducible research - tools essential for understanding, monitoring and improving cities around the world. 

Entry Requirements

Candidates must have a sponsoring employer based in England who will employ them in a spatial data science or geographic information science role and is prepared to provide a work place mentor and enter into an employer agreement with UCL. Applicants who do not have this arrangement may be interested in applying for the Urban Spatial Science MSc instead.

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Fees

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Programme Funding

UCL offers a range of financial awards aimed at assisting both prospective and current students with their studies.

Student Destinations

This course provides students with the technical skills and knowledge base to embark on a professional or academic path through the highly interdisciplinary field of urban spatial science and the wider urban planning and policy fields. Graduates from our department have gone on to pursue a wide variety of careers in local government, urban planning, software development and academic research (Graduate Outcomes Surveys 2017-22). 

Module Details

Compulsory modules

  •  Urban Systems Theory
  •  Geographic Information Systems and Science
  •  Quantitative Methods
  •  Foundations of Spatial Data Science
  •  Research in Practice
  •  Urban Spatial Science Dissertation

Optional modules

  •  Urban Simulation
  •  Group Mini Project: Digital Visualisation
  •  Data Science for Spatial Systems
  •  Smart Cities: Context, Policy and Government
  •  Agent-based Modelling for Spatial Systems
  •  Remotely Sensing Cities and Environments
  •  PropTech: Introduction
  •  Building Spatial Applications with Big Data

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