Predictive Analytics for Climate-Sensitive Sectors
Leveraging Data-Driven Forecasting for Resilience, Risk, and Resource Optimization
Skills you will gain:
This intensive international course is designed to develop practical skills in predictive analytics applied to climate-sensitive sectors including agriculture, water, energy, and public health. Through interactive sessions using real-world datasets and freely available tools, participants will learn to build predictive models, visualize environmental risks, and support climate-resilient decision-making.
Aim: To equip participants with the analytical tools, techniques, and models required to forecast climate-related risks and opportunities in critical sectors, enabling informed planning, sustainable resource management, and policy resilience.
Program Objectives:
What you will learn?
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Week 1: Climate Data and Predictive Analytics Foundations
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Global overview of climate-sensitive sectors and their vulnerabilities
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Introduction to predictive analytics: regression, classification, time series forecasting
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Understanding and accessing open climate data sources (NASA EarthData, NOAA, Copernicus, IPCC)
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Data preparation techniques: missing values, temporal formatting, spatial tagging
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Visualizing climate trends using Python and Jupyter Notebooks
🛠️ Hands-on Tools: Python (Pandas, NumPy, Plotly), Jupyter Notebook
Week 2: Predictive Modeling and Geospatial Analysis
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Machine learning techniques for climate data: ARIMA, Random Forest, XGBoost
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Predictive modeling for rainfall, temperature, and crop yield
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Introduction to geospatial data: raster vs vector, spatial layers, map overlays
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Climate zoning and risk area detection (e.g., drought, floods)
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Model development using real datasets, including training, testing, and validation
🛠️ Hands-on Tools: Scikit-learn, GeoPandas, Folium, QGIS, Google Earth Engine
Week 3: Applications, Dashboards, and Capstone Projects
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Review of global case studies: agriculture forecasting, hydrology, health risk models
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Building a full predictive pipeline: data ingestion → modeling → evaluation → visualization
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Model evaluation metrics: MAE, RMSE, R², bias assessment
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Designing interactive climate dashboards using Streamlit
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Group-based capstone project: Presenting a predictive solution for a real-world climate issue
🛠️ Hands-on Tools: Streamlit, GitHub, Python (Seaborn, Altair, Matplotlib)
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Intended For :
- Climate scientists and environmental engineers
- Professionals from agriculture, energy, water, or public health sectors
- Data analysts and AI/ML practitioners in sustainability domains
- Government and policy planners
- Postgraduate students and researchers in climate and data sciences
Career Supporting Skills