A new report has been published. You can access all reports by clicking on Classroom and navigating to Section 6.2. This report explains what is happening in the U.S. electricity system during the “energy transition” (the long-term shift away from coal, oil, and gas toward cleaner power like solar and wind). It shows that even if federal politics becomes less supportive of renewables, the core trend still looks similar: electricity demand keeps rising, solar and wind keep growing, and coal keeps shrinking. It also breaks down, simply, why this continues to happen. The report has been written using the following sources, along with extra details that I added. Reading industry reports like this one is very helpful because it helps us demonstrate energy market awareness (economics, finance, trends). You can expect 1-2 such reports every week, focusing on key economic trends in the energy sector (extremely valuable for interviews, networking, and demonstrating expertise). The report has been written using the following sources, along with extra details that I added. The Economist: https://www.economist.com/business/2025/09/16/despite-presidential-animus-americas-solar-industry-is-buzzing? Financial Times: https://www.ft.com/content/0f0327f1-573a-4336-bed4-b08a5e04c6b3? Bloomberg: https://www.forbes.com/sites/kensilverstein/2025/03/06/renewables-will-best-fossil-fuels-over-time-despite-trumps-efforts/? Wall Street Journal: https://www.wsj.com/articles/solar-projects-face-turmoil-under-trump-but-big-business-is-still-banking-on-them-e68e397f

I’ve just published a new online course about Memory-Efficient Processing of Big Data. This course teaches real-world skills as they are used in practice. Smart meters measure the electricity-consumption data every hour, and store the information in CSV files. These files eventually become very large (big data). The new online course is called "Smart Meter Big Data Efficient Processing" and it is in the Classroom in 1.36. This online course teaches a Python methodology that is used by energy companies in practice to read extremely large datasets (Big Data). Without this technique such files cannot be read because they cause a memory (RAM) error. Companies that sell electricity to consumers are known as 'Retailers' or 'Suppliers'. Such companies have CSV files with hundreds of millions of rows, where each row is the hourly kWh electricity consumption. If they try to load these CSV files, their computers will run out of RAM and crash. So these companies process these files using Python iterators, which enable a memory-efficient and fast processing method. In this course, I show you the industry-standard solution: using Python Iterators to process Big Data in "chunks". See the attached image; this is analysed in detail in the course.

Course practice 1.35 This course, with its Python exercise, allows you to visualise 10 years of global trends and developments in clean energy production by major region (Africa, China, Europe, etc.). 1/ World 2/ Africa The improvements are clear to see!

Online Course 5.20 is Now Live in the Classroom! The course includes a PowerPoint presentation, full Python implementation and many hours of video lectures. It is a beginner-friendly course on the valuation of Commodity Options (like options on crude oil), including full Python implementation. No stochastic calculus, and no fancy "martingale" mathematics. Just simple, practical language, explaining exactly how it is used in practice, in finance. Banks like JPMorgan use these exact models to help oil companies guarantee their income, even if oil prices crash. On the other side, oil refineries use this same math to lock in cheap fuel prices. What we need to remember (also great for interviews): When a company exercises a Commodity Option, it doesn't get the commodity (e.g. oil) immediately. Rather, it receives a Futures Contract. Once that contract expires, it then receives the commodity ( crude oil). So: Option --> Futures --> Commodity. The attached cheat sheet compares the two main pricing models in energy finance: 1. Black-Scholes: Used for Energy Equities (Stocks). 2. Black-76: Used for Commodities (Futures). What’s Inside Course 5.20: - Foundations: Spot Price vs. Futures Price vs. Strike Price. - The Math: Calculating "Premium" using the Black-76 Model. - The Code: Full Python implementation for pricing Call and Put options. - Valuation: Understanding Intrinsic vs. Extrinsic (Time) Value. - Mechanics: American vs. European options and Physical Settlement. - Real World: How an Option becomes a Future, and how a Future becomes Oil.

The attached plot shows the level of electricity in Africa. How best can it increase? using smart grids? micrograms?