A Workshop on Leveraging Data-Driven Forecasting for Resilience, Risk, and Resource Optimization
This intensive international workshop 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.
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.
Global overview of climate-sensitive sectors and their vulnerabilities
Introduction to predictive analytics: regression, classification, time series forecasting
Understanding and accessing open climate data sources (NASA EarthData, NOAA, Copernicus, IPCC)
Data preparation techniques: missing values, temporal formatting, spatial tagging
Visualizing climate trends using Python and Jupyter Notebooks
🛠️ Hands-on Tools: Python (Pandas, NumPy, Plotly), Jupyter Notebook
Module 2: Predictive Modeling and Geospatial Analysis
Machine learning techniques for climate data: ARIMA, Random Forest, XGBoost
Predictive modeling for rainfall, temperature, and crop yield
Introduction to geospatial data: raster vs vector, spatial layers, map overlays
Climate zoning and risk area detection (e.g., drought, floods)
Model development using real datasets, including training, testing, and validation
🛠️ Hands-on Tools: Scikit-learn, GeoPandas, Folium, QGIS, Google Earth Engine
Module 3: Applications, Dashboards, and Capstone Projects
Review of global case studies: agriculture forecasting, hydrology, health risk models
Building a full predictive pipeline: data ingestion → modeling → evaluation → visualization
Model evaluation metrics: MAE, RMSE, R², bias assessment
Designing interactive climate dashboards using Streamlit
🛠️ Hands-on Tools: Streamlit, GitHub, Python (Seaborn, Altair, Matplotlib)
₹2,499
$50
The recordings are being uploaded. They appear in your dashboard as soon as they are ready, and we will e-mail you then.
Through recorded video lectures from the original live workshop, with the study material. Watch at your own pace, any time.
365 days from your registration, on your NanoSchool dashboard.
Yes, an e-certificate after completion, with a QR code anyone can verify and an "Add to LinkedIn" button.
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy
Registration open
Live, mentor-led and certified. Dates and times in IST.
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