Foundations of Energy Data Science

The energy sector is undergoing a massive transformation driven by data. This course provides a practical, text-based introduction to how data science is used to optimize power grids, predict demand, and build a more sustainable energy future. You will move from foundational concepts of the electricity market to applying core data analysis and machine learning techniques. By the end, you'll be equipped to process, analyze, and interpret energy-specific datasets to uncover valuable insights and build foundational predictive models. What you'll learn: - Understand the key concepts of the electricity sector and the types of data it generates, from smart grids to market prices. - Apply Python with common data science libraries to clean, process, and explore energy consumption and generation data. - Build time-series models to forecast electricity demand and identify seasonal patterns. - Learn the principles of anomaly detection and predictive maintenance for power grid assets using machine learning concepts. - Practice data visualization techniques to effectively communicate findings about grid performance and energy trends. - Master the fundamentals of feature engineering specifically for energy-related datasets. The course begins with the essential terminology of the energy industry before guiding you through hands-on written exercises in data preparation, exploratory analysis, and building foundational models. You will learn by reading clear explanations and practicing with code examples. This course is designed for beginners. No prior experience in the energy sector or data science is required; all you need is a curiosity to learn. Start your journey into the world of energy data science today.