Official course description, subject to change:
Basic info last published 1/10-23

Geospatial Data Science

Course info
Language:
English
ECTS points:
7.5
Course code:
KSGEDAS1KU
Participants max:
40
Offered to guest students:
yes
Offered to exchange students:
yes
Offered as a single subject:
yes
Price for EU/EEA citizens (Single Subject):
10625 DKK
Programme
Level:
MSc. Master
Programme:
MSc in Data Science
Staff
Course manager
Michael Szell
Associate Professor
Course semester
Semester
Forår 2024
Start
29 January 2024
End
23 August 2024
Exam
Abstract
This course provides an introduction into core concepts and applications of data science based approaches to geospatial data analysis.
Description

Geospatial data is ubiquitous. Massive geospatial data are generated every second from our smartphones, through our social media posts, or through many kinds of other means like tracked whale trajectories in the ocean, allowing us to trace the movements of entire societies. As these data keep growing, it becomes more important to extract meaningful insights from location, relation, and position, for applications as diverse as business analytics, epidemiology, or species protection.

This course provides students core competences in Geospatial Data Science (GDS). This includes the following:

  • Data structures and principles of GIS; map projections and measurement
  • Gathering and preprocessing large-scale geospatial data
  • State-of-the-art computational tools for GDS
  • Spatial network analysis
  • Main methodologies available to the Geospatial Data Scientist, as well as their intuition as to how and when they can be applied
  • Real world applications of these techniques in an applied context

Formal prerequisites

A prerequisite for taking this course is solid know-how in Python programming and data analysis.

Intended learning outcomes

After the course, the student should be able to:

  • Demonstrate GIS/GDS concepts and be able to use relevant Python libraries programmatically to import, manipulate and analyze spatial data in different formats. Apply a number of spatial analysis techniques and explain how to interpret the results, in a process of turning data into insights.
  • Reflect on the motivation and inner workings of the main methodological approaches of GDS, both analytical and visual.
  • Critically evaluate the suitability of a specific GDS technique, what it can offer and how it can help answer questions of interest.
  • Apply a number of spatial analysis techniques and explain how to interpret the results, in a process of turning data into insights.
  • When faced with a new data-set, work independently using GIS/GDS tools programmatically to extract valuable insight.
Ordinary exam
Exam type:
C: Submission of written work, Internal (7-point scale)
Exam variation:
C1G: Submission of written work for groups