Level: Advanced / Professional
Duration: 10–12 Weeks (Approx. 60–80 hours)
Delivery: Hybrid (Live Virtual Sessions + Practical Labs + Capstone Project)
Prerequisites:
- Basic statistics and Python programming
- Familiarity with GIS (QGIS or ArcGIS)
- Understanding of agriculture, environment, or economics fundamentals
Course Goal
To train experts capable of applying data analytics, geospatial technologies, and predictive modeling to improve agricultural productivity, food system efficiency, and climate resilience.
Learning Outcomes
By the end of this course, learners will be able to:
- Acquire, clean, and analyze agricultural and food security datasets from multiple sources.
- Apply geospatial and temporal analytics to monitor crop yield, rainfall, and soil conditions.
- Build predictive models for drought and food insecurity forecasting.
- Design dashboards and early warning systems for agricultural decision-making.
- Integrate economic, climatic, and nutritional data for SDG 2 monitoring and policy formulation.
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