About Workshop
Aim
What Participants Will Learn
- Empower professionals with AI and statistical tools to manage climate impacts
- Bridge the gap between raw climate data and actionable planning
- Enhance data-informed decision-making in national development plans
- Promote sectoral resilience through proactive prediction and simulation
- Foster interdisciplinary collaboration between climate science and AI
Structure
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Day 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
Day 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
Day 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
🛠️ Hands-on Tools: Streamlit, GitHub, Python (Seaborn, Altair, Matplotlib)
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Important Dates
Registration Ends
Workshop Dates
What You Will Gain
Outcomes
- Build predictive models using real-world climate and sectoral data
- Use data science to anticipate sector-specific risks and responses
- Develop early warning indicators and forecasting dashboards
- Gain fluency in climate analytics tools, platforms, and ethical practices
- Receive a certificate in “Predictive Analytics for Climate-Sensitive Sectors”
Who Should Attend
- 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