Description: This module introduces students to scientific programming concepts and tools essential for ecological forecasting. It provides a foundation for the modelling and data analysis skills developed in later modules. Students learn the principles of professional, collaborative, and reproducible programming through practical exercises. The module focuses on coding practices relevant to ecological modelling and data science, with hands-on experience in R, Python, and C++, and in using tools such as VS Code, Git, and GitHub within an open-science framework.
Learning outcomes:
Apply fundamental programming concepts and workflows to ecological modelling and data analysis tasks.
Write, document, and debug code in R, Python, and C++ using professional development environments.
Use version control systems (Git, GitHub) to manage code collaboratively and transparently.
Implement programming practices that support reproducible and open scientific research.
Evaluate and select appropriate programming tools and approaches for solving specific ecological forecasting problems.
Module group: Foundations of Ecological Forecasting
Credits: 5 ECTS
Lecturers: Prof. Dr. Andreas Hotho, M.Sc. Daniel Weggenmann