In the interview , for a company (energy investments / commodities) a panel of 3 interviewers asked me the following during a discussion . Panel: Let's talk about some important Python details. It's ok if you don't remember things. Just an approximate answer will be fine for us. So, in Python, what is the difference between a List comprehension and a Generator expression. In a few words... Correct answer: A generator expression is like a list comprehension, except that it doesn't store the list in memory. Panel: Give some example . Write here in the tablet. Answer: list comprehension: simulated_returns = [price * volatility for price in historical_data] portfolio_value = sum(simulated_returns) generator: simulated_returns = (price * volatility for price in historical_data) portfolio_value = sum(simulated_returns)

Few professionals in energy also read research publications. There are over 200 such analysis in sections 6.1 and 6.4 in the Classroom. Below is a popular paper connecting solar PV economics, machine learning and energy storage. Below there is also a brief summary. Scroll down to download it. - Title: Optimizing distributed solar energy economics: A machine learning classification approach to storage system management - Citation: Wenhui, L., Li, H., Laghari, M. A., Guliyeva, S., Amonova, N., & Yuan, H. (2025). Optimizing distributed solar energy economics: A machine learning classification approach to storage system management. Case Studies in Thermal Engineering, 75, 107106. https://doi.org/10.1016/j.csite.2025.107106 - Downloadable resource is attached below Key Points: Solar energy is a clean resource, but it faces a major challenge because the sun does not shine consistently. This makes it difficult to match the amount of electricity generated with the amount people need to use. Batteries are used to store extra energy for later, but they are expensive and can wear out if used incorrectly. Traditional methods for managing this storage often fail to look at both the technical needs and the financial costs at the same time. The researchers in this study developed a new method to solve these problems using a type of artificial intelligence called K-Nearest Neighbors or KNN. This is a classification tool that looks at past examples to decide what to do in the current situation. Instead of only focusing on the technical side of things, this new system includes economic factors. It helps the system decide the best times to charge or discharge the battery to save the most money. The study uses a concept called entransy dissipation theory alongside the computer model. This is a physics principle that measures the loss of energy quality during transfer processes. By using this theory, the system ensures that energy is not just stored, but is kept at a high quality. This approach balances the need to save energy quantity with the need to maintain its usefulness, which helps in reducing the overall costs of running the system.

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.

Since linear and logistic regression are supervised models and are frequently used for exploratory analysis in data mining, would it be accurate to say that the distinction between data mining and machine learning is primarily methodological (discovery vs prediction) rather than algorithmic? Please feel free to share any experiences choosing to use data mining for Energy or any other industry.

I’m sharing a career guide on consulting paths for Energy Data Scientists. It breaks down the three main routes: Strategy, Technical/Specialist, and Implementation consulting. And it explains what the work looks like, typical deliverables, and the skill sets needed. PDF attached. All career guides are in Classroom --> 6.3.