Gain essential skills for environmental data science and water digitalization.
This course covers:
- Reproducible scientific computing (bash, Git, GitHub)
- Open geospatial data & environmental data structures
- Supervised machine learning for space/time applications (R & Python)
- Computational pipelines & repository design
- Best practices for HPC in JupyterHub environments
As part of the course, you will make progress on a portion of your thesis, learn to access the DIWA computing resources, and implement reproducibility and collaboration best practices in your computational work, and deepen your relationships with your colleagues. Because the courses is project-based, it is suitable for coders of all levels -- any DIWA researcher can benefit from implementing the skills and principles we will cover.
- Teacher: Elsa Culler