Course Overview
On this postgraduate geographic data science course you will learn to apply data science, machine learning and geographic information system (GIS) techniques. This will enable you to gather, analyse and extract insights from geospatial data to uncover the underlying patterns driving various urban and natural environments.
Why choose this course?
- This course is ideal for developing your quantitative data analysis skills to answer questions on spatial, environmental and geographic social science topics.
- It will equip you with the tools to analyse large datasets and make predictions based on machine learning algorithms.
- It benefits from valuable insights from leading academics and encourages your independent thought on the nature of spatial data in society.
- You will acquire practical skills to apply GIS in managing and analysing spatial data across several fields, from social to environmental sciences.
What you will learn
Using cutting-edge tools and technologies, this course offers you technical and transferable skills for manipulating and analysing spatial data related to natural and human phenomena.
You will explore several analytical techniques, approaches and case studies to learn how to effectively gather, manage, visualise and analyse large spatial datasets from various social, urban and environmental domains.
You will also learn to use programming languages and specialised software to create spatial machine-learning models that address some of society's major challenges.
How you will learn
You will learn through lectures, practical sessions and online resource activities. This course is available to study full- or part-time. It has an evening timetable with classes taking place in the evening.
We offer this course as a Master’s, Postgraduate Diploma and Postgraduate Certificate. For the Diploma and Certificate you study fewer modules and do not complete a dissertation. The Diploma and Certificate are aimed at professionals and researchers who work with, or wish to work with, geographically referenced data. They are ideal if you are interested in studying at postgraduate level for personal or professional reasons, but don't yet want to commit to a full MSc.
