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Keep shoppingCO₂ Emissions Forecasting with Shallow Neural Nets in Python
1,421 students
CO₂ Emissions Forecasting with Shallow Neural Nets in Python
Build Accurate Time Series Forecasts with Python - Energy Sector Application
Created byAlgorithmic Economist, PhD
Last updated 6/2026
English
What you'll learn
- Build Shallow Neural Network models to forecast CO2 emissions using Python
- Apply a proven 10-step methodology for creating statistically sound and reliable forecasts
- Work with real World Bank data to analyze emissions trends for India, China, USA, UK, EU and global averages
- Master essential statistical tests including overfitting analysis, naive model benchmarking, and sensitivity analysis
- Quantify forecast uncertainty using confidence intervals and error metrics like MAPE
- Create publication-ready visualizations of historical trends and future projections
- Understand when Shallow Neural Network modelling is appropriate for time series forecasting vs other methods
- Implement best practices for model validation, hyperparameter tuning, and results interpretation
Explore related topics
Course content
7 sections • 29 lectures • 4h 27m total length
Requirements
- Absolute beginners welcome!
- You'll receive the full Python code, which you can adjust to your own projects
- No programming experience? Follow along and learn by doing
- No statistics background needed
- Just need a computer and enthusiasm
Description
WHO I AM: I hold a PhD in Quantitative Economics and Energy from Imperial College London. I teach practical, real-world data science specifically for the energy sector.
REGULAR ENHANCEMENTS: This course is reviewed periodically with updates to reflect the modern energy market.
STUDENT BONUS: Note: Students who enroll in this course will receive access to the Energy Data Scientist community.
What You'll Learn:
How to build a Shallow Neural Network model in Python that can forecast CO₂ emissions
How to achieve high accuracy in the forecasts that you will produce
How to work with World Bank historical data
How to implement advanced statistical tests
How to apply your model to real-world cases (India, China, USA, UK, European Union analysis)
Perfect For:
Environmental consultants and analysts
Energy economists and policy makers
Data scientists in sustainability
Climate professionals
Why This Matters:
With net-zero targets and mandatory carbon reporting, professionals who can produce credible emissions forecasts are in high demand. Master the skills that set you apart in the growing climate economy. Companies now require carbon footprint assessments for regulatory compliance and ESG reporting. Governments need emissions projections for policy planning. Consultancies charge premium rates for these capabilities. Whether you're advancing your current career or transitioning into sustainability, these practical forecasting skills open doors to roles paying $150,000-250,000+ in the rapidly expanding green economy.
Who this course is for:
- Environmental/Climate Analysts seeking quantitative forecasting skills
- Sustainability Professionals needing to project emissions for reporting
- Energy Sector Professionals wanting data-driven analytical methods
- Graduate Students & Researchers in environmental science, energy, or climate studies
- Data Scientists/ML Engineers moving into climate and energy applications
- ESG Analysts & Consultants requiring emissions projection capabilities
- Policy Analysts working on climate strategies and carbon reduction plans
- Anyone transitioning to climate tech who needs practical forecasting skills