Outline

Observational, satellite, and reanalysis datasets • Climate model outputs and scenario datasets • Geospatial and temporal data structures • Data preprocessing, cleaning, and quality assessment

Outline

AI and machine learning concepts for environmental systems • Supervised and unsupervised learning approaches • Feature engineering for climate variables • Dimensionality reduction and data preparation workflows

Outline

Heatwaves, floods, droughts, storms, and climate hazards • Event detection and classification methods • Forecasting and anomaly identification • AI-based pattern recognition in extreme-event analysis

Outline

Climate scenarios and emissions pathways • Forecasting future environmental conditions • Sensitivity analysis and uncertainty interpretation • Comparing scenario outcomes under different assumptions

Outline

Linking climate outputs to policy and planning questions • Adaptation and mitigation strategy evaluation • Climate-risk interpretation for infrastructure and communities • Decision-support frameworks for environmental governance

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Python and R workflows for climate analysis • Data visualization, dashboards, and impact communication • Reproducible computational practices • Workflow design for research, consulting, and institutional use

Outline

Extreme-event prediction case studies • Regional climate-risk assessment exercises • Policy-oriented scenario simulation projects • Final reproducible workflow for climate and policy analysis