Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
After completing this course,
- students will understand and deepen their knowledge of a range of modern spatial data science (SDS) techniques and methodologies
- students are able to apply SDS methodologies in practice to a range of sustainability/global change related topics with Python programming language
- students have skills and understanding how to retrieve, handle and analyze modern big geospatial data in different formats
Schedule: 11.01.2021 - 26.02.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Henrikki Tenkanen
Teacher in charge (applies in this implementation): Henrikki Tenkanen
Contact information for the course (valid 13.11.2020-21.12.2112):
Professor Henrikki Tenkanen (email@example.com)
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English, Languages of attainment: English, Finnish or Swedish
CONTENT, ASSESSMENT AND WORKLOAD
The course provides a deep insight to modern spatial data science methods which are applied to various topics/questions relating to sustainability and global change. The course covers techniques and modeling approaches for vector and raster data formats as well as for data presented using networks.
Assessment Methods and Criteria
Assignments and final report
Lectures (20 h), self-study (20 h), assignments (50 h), final report (45 h)
All the materials are distributed on a dedicated open access website with lecture notes, online programming tutorials and literature.
GIS-E1030 Introduction to Spatial Methods, or similar knowledge