I see many students who want to stay home and so they ask for remote work all the time. Also I see many who go to work and do not dress well. Here is the truth for the job: Truth1: Go 3-4 times every week to the office so others see you. Your presence is very important. Smile and be professional. This plays a role for your promotion. If you stay home most of the time, you will not be promoted and people will forget you. Truth2: Do not use AI a lot at workplace because every computer has trackers and gives your managers a distribution of time (with plots) of how much you used AI and for how long. There is a software (hidden) that summarises your activity on the computer at work. So be careful : if they see that you cannot write code, and all you do is copy-paste from Chat GPT the code, they will fire you sooner or later. Silently one day they will fire you and they will not tell you why most likely. Be careful. Also you can use it on your phone, but it is time consuming.. Ofcourse they have AI tools but you must write code yourself. Truth3: If you go to non-code positions, it is more stressful and more competitive than code positions. Often, you get lower salary also. Because people in energy are scared of coding, and do not like it. So you get an advantage if you can code , understand code etc. Not super . Just basic things. Eg understand Python . Understand ML. etc Truth4: Do not share personal stories with co-workers. They are competing with you for promotion so they want you to fail ... Be careful. Do not share sensitive things. Truth5: Becareful of your social media presence. Managers spy on you e.g. they have fake profiles and are your friends. They will find your second, third etc profiles. They can find your anonymous X profile where you troll people . They have the ability to find you because they have software tools that you do not know . Truth6: yes the online courses here are all you need. But please fix your CV. Align it with energy companies. Do not just design your CV yourself. Get feedback here. Most CVs look rubbish. Try work on your CV carefully.

I pulled together some notes from recent discussions on employment conditions across countries, especially for energy/utility roles. Sharing here in case it helps anyone comparing markets. I have attached the Excel file. The file is broader employment/job-market comparison by country, and only one part of it is energy-related via the “Utility Sector Security” column.Useful if you’re comparing job stability vs compensation in the broader energy sector. Big picture: - USA = highest salaries, but weaker general job security and social safety net - UK / Australia = strong employment protections and very stable utility-sector roles - France = strongest labor protections and very hard to dismiss employees - Switzerland = very high salaries with a strong financial safety net

I’ve uploaded a new report on the UK Power Market. It gives a practical overview of how the market works across each layer, from long-term contracts and day-ahead trading to intraday markets, balancing, and ancillary services. It also highlights the most important recent reforms, pricing trends, and the structural features shaping the market today. It is like a cheat sheet, concisely giving an overview of a complex market . The UK market is often seen as a model market because it is one of the most mature, transparent, and actively reformed power markets in the world. So, understanding how it operates helps bring much more clarity to power markets globally. The UK markett is moving fast on market reform, which makes it a very useful case study for where global power markets may be heading. You can find and download the report in the Classroom, under Energy Industry Support ( a special section focused on analysing trends and key topics in the energy industry using simple language). Attached is a screenshot from the report.

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

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.