Here is a plot about hydrogen in the United Kingdom. The plot shows that energy factories need steady, very high temperature heat, around 1500°C, running continuously, and today they mainly burn methane gas. Their plan is to switch to low carbon hydrogen, either ‘blue hydrogen’ made from natural gas with carbon capture and storage or ‘green hydrogen’ made by splitting water using renewable electricity. But the central barrier is cost and complexity: hydrogen is still expensive to produce, transport and store, and it requires coordinated investment across multiple industries and public infrastructure.

The term 'Energy Data Scientist' means more than just a Data Scientist who works in an Energy Company. Given the skills that this Skool community teaches, what jobs does the Energy Data Scientist cover?

Below is part of some consultancy work I completed recently and sharing a few key ideas: Renewable energy is growing but fossil fuels are still growing too, so the system is adding rather than replacing. Oil and gas will remain important for a long time, so new investment in fossil‑fuel supply and infrastructure (pipelines, power plants, refineries, and other big long‑lasting equipment) will still be needed. If clean energy supply grows faster than total demand for long enough, clean energy first covers new demand and then starts replacing fossil fuels. If clean energy supply keeps outgrowing demand by a few percentage points for decades, fossil fuels will eventually be squeezed out. Real replacement of fossil fuels, not just adding clean energy, is not guaranteed. Most likely we will see 'addition' and co-existence of fuels. This is because the demand is keep rising more and more eg due to AI etc. See the plot .

Hey everyone, I’m asking this because LLMs weren’t really part of our workflow before, but now that they’re available it’s difficult not to leverage them given their productivity benefits. How are you using LLMs in data science workflows, and what best practices should I be aware of? Also, from a manager’s perspective, what expectations or concerns do you have about their use? PS: I am one of those that do not use it much especially for work or academia and looking for the correct ways to integrate it.

This weekend I visualized how electricity access has changed across West Africa over the last 18 years — and the progress is striking, though uneven. Ghana – Near-universal access. A regional benchmark. Côte d’Ivoire – Strong, consistent growth into the top tier. Nigeria – Big improvement, but population size keeps the gap wide. Senegal – Solid gains driven by energy reforms. Mali & Burkina Faso – Progress, but still constrained by infrastructure. Niger – Improvement visible, yet access remains very low. Sierra Leone & Liberia – Large relative gains from low starting points. West Africa is advancing — but where you live still determines whether you have power. I’m excited to continue exploring how data can drive better decisions in energy, development, and policy. Which country’s progress surprised you the most? Data source: World Bank