Deep Learning for timeseries forecasting of Carbon Emissions

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2. The course gets updated every 6-12 months. Visit often to download new material!

3.Course Overview: This course teaches how to model and apply deep learning—specifically deep neural networks—for time series forecasting, with a focus on CO₂ emission predictions. While the application centers on environmental data, the modeling principles apply broadly to other time series problems. You will follow a clear, step-by-step process used in real-world practice, providing practical insight into how deep learning is actually applied in industry and research. The course uses real data from the World Bank to forecast emissions across key global regions, including China, the U.S., India, the EU, and more. Through hands-on case studies and large-scale projects, you'll gain experience working with real datasets and develop the skills to deploy forecasting models in realistic settings. Repetition and real-world focus help reinforce core techniques and improve your confidence. You will also enhance your ability to interpret model outcomes in both academic and applied contexts.  Deep learning has become a powerful tool for forecasting complex patterns in time series data, making it essential for professionals dealing with climate modeling, energy systems, finance, and beyond. Learning how to apply it correctly bridges the gap between theoretical knowledge and practical implementation—something highly sought after in both academic research and data-driven industries. This course is ideal for students in data science, engineering, and environmental studies; aspiring energy economists or climate analysts; and professionals working in policy, consulting, or sustainability-focused roles. Careers that benefit include data scientists, machine learning engineers, environmental modelers, climate policy analysts, and energy forecasters. With increasing emphasis on climate data and predictive modeling, these skills open doors in think tanks, international organizations, startups, utilities, and government agencies tackling global environmental challenges.