BIOGRAPHY

A team (PhD) specialized in teaching Data Science (including artificial intelligence, machine learning, and optimization) for the energy sector (investments, economics, etc.). Visit the official website of the Energy Data Science Academy to find online courses about data science applications in the Energy Sector. Visit www [dot] energydatascience [dot] com.

THE COURSE IS UPDATED EVERY 6-12 MONTHS. VISIT OFTEN! 

The content (videos/code) is updated every 6-12 months (e.g. new publications become available for download). Come back often! 

ANYTIME YOU NEED HELP, SEND A PRIVATE MESSAGE OR USE THE Q&A FORUM. YOU WILL GET A REPLY WITHIN HOURS FROM THE INSTRUCTOR!

You can send a private message or write your question in the Q&A forum inside the course!

FIND HUNDREDS OF COURSES 

Visit www [dot] energydatascience [dot] com and explore hundreds of specialized courses!

WHAT THIS COURSE IS ABOUT

1. Learn how to model and apply deep learning to forecasting; the application is on CO2 forecasts - but the principles are the same for any other context.

2. Learn the exact step-by-step approach by which deep learning is applied IN PRACTICE. This approach is highly valued in academia, industry, and research.

3. Apply your model for the forecasting of CO2 emissions in China, USA, UK, France, India, etc using real-world Worldbank datasets - exactly as it is done in practice! 

4. Apply your learning through practical examples, real-world case studies, and industry-relevant projects, equipping yourself with the skills and confidence to use these tools effectively in professional and academic contexts.

Who this course is for:

• Data scientists and analysts interested in applying deep learning techniques to environmental data and forecasting.
• Climate researchers and environmental professionals looking to enhance their skills in predictive modeling for CO2 emissions.
• Python programmers and developers eager to learn how to build time-series forecasting models using deep learning frameworks.
• Policy makers, energy analysts, and sustainability consultants who need accurate long-term CO2 forecasts to inform decision-making.
• Graduate students or academics in fields such as environmental science, data science, or machine learning, seeking practical applications in forecasting.