Competence Framework
This course develops four complementary areas of competence. Students build subject-specific knowledge of algorithmic environmental governance challenges while developing method-specific skills in data science, optimization, and causal inference, alongside social and personal competencies needed for collaborative, reproducible, and responsible data science practices.
Subject-Specific
- Understand major environmental management and policy challenges across biodiversity, climate, restoration, and land-use governance.
- Interpret how environmental data are used in monitoring, reporting, evaluation, and policy design.
- Recognize how indicators, models, and digital infrastructures shape environmental governance.
- Connect data analysis to real policy problems such as conservation prioritization, adaptation planning, and impact evaluation.
Method-Specific
- Work with tabular, spatial, and text data using reproducible computational workflows.
- Use big-data and cloud-native approaches to manage, query, and analyze environmental datasets.
- Apply causal inference concepts for environmental policy impact evaluation.
- Apply optimization and decision-support methods for strategy design under constraints.
- Build and assess AI-assisted and platform-based workflows for environmental analysis.
Social
- Collaborate effectively in teams to define problems, divide analytical tasks, and synthesize findings.
- Communicate technical results clearly to policy, interdisciplinary, and public audiences.
- Engage constructively with domain, stakeholder, and data science perspectives.
- Debate the governance implications of data infrastructures, platforms, and algorithmic decision systems.
Personal
- Develop critical judgment about data quality, uncertainty, bias, and model limitations.
- Reflect on the politics of data, platform ownership, and the distribution of power in environmental governance.
- Practice responsible, transparent, and ethical use of computational tools, including AI systems.
- Build confidence in designing, defending, and revising data-driven analyses in complex policy settings.
Learn more about the ETH Competence Framework.