About this course
Course description
This course introduces essential methods of the data science workflow, supported by the book Applied Geodata Science and demonstrates common applications of machine learning methods in Geography and Environmental Sciences by worked examples.
Learning objectives
- Implement a data science workflow for common domain-specific machine learning-based modelling tasks.
- Analyse and visualise models and their predictions and communicate insights.
- Describe the challenges of model fitting and evaluate model generalisability.
- Adopt and benefit from Open Science practices and resources for data-intensive research projects.
Course contents
This course covers all steps along the data science workflow (Figure 1) and introduces methods and tools to learn the most from data, to effectively communicate insights, and to make your workflow reproducible. By following this course, you will be well equipped for joining the Open Science movement.
Target audience and prerequisites
Bachelor students in Geography and in Climate Sciences at the University of Bern. This course is a product of the group for Geocomputation and Earth Observation, Institute of Geography, taught by Benjamin Stocker and Fabian Bernhard. The course content was established with contributions by Koen Hufkens, Pascal Schneider and Pepa Aran.
