Postgraduate taught 

Computational Geoscience MSc

GES_Spatial Data Analytics GEOG5132

  • Academic Session: 2023-24
  • School: School of Geographical and Earth Sciences
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: Yes

Short Description

This course provides students with an introduction to geospatial data science with aims to explore, measure, analyse, and model spatial distribution of geospatial data using statistical methods and programming tools.


Second half of Semester 1.

5 weeks. 2 hours of lecture per week and 2 hours of practical per week

Excluded Courses





1. Weekly small exercises together with a reflection report (40%)

2. A final individual spatial data science project (report 40% + presentation 20%)

Course Aims

■ To examine characteristics of geospatial data distribution.

■ To extract knowledge and insights from geospatial data using statistical methods.

■ To develop skills in processing and analysing and presenting geospatial data using modern data science pipeline.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ Explain the characteristics of geospatial data.

■ Critically analyse appropriate statistical methods to investigate geospatial data pattern.

■ Process, analyse, visualise geospatial data using programming tools (e.g. Python).

■ Apply learned practical skills on real-world datasets.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.