Predictive Analytics for Climate-Sensitive Sectors

Aim

Program Structure

• Week 1: Climate Data and Predictive Analytics Foundations

  • 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

  Week  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

  Week 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

  • Group-based capstone project: Presenting a predictive solution for a real-world climate issue

  🛠️ Hands-on Tools: Streamlit, GitHub, Python (Seaborn, Altair, Matplotlib)

Who Should Enrol?

Fee Structure

We accept 20+ global currencies. View list →

What You’ll Gain