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)

Speaking of interview rounds, I am sharing a very classic 'tricky' and frequently occurring question asked to those applying for consultancy roles in energy for entry roles . By 'entry roles' I do not mean internship. You need to have a BSc for sure, and you aim for a salary around $120k gross annual revenue - speaking of the United States , for 0-1 years of experience , i.e. fresh graduates. But this question is also asked to intermediate experience candidates e.g. 2-5 years experience (salary around. $180k / year in the USA. This is gross ie pre-tax, and annual). Below you see the plot of an electricity demand profile i.e. shows the electricity demand over a day. First, the question is to explain what we see. What do we understand in this plot. To explain the ups and downs. They may point with their finger e.g. 'why here it is low ' , 'why there it is high', speaking of this blue line. And then explain what happens to the price of electricity. Can we infer the wholesale electricity price from this plot? Because this plot doesn't show the price. It shows the demand . And this is the total demand in that region. Electricity demand. This question is asked because when you' re a consultant, your client often asks such questions. And you have to reply fast and accurately, or you may risk your reputation. If you try to evade e.g. by changing topic, then your client isn't stupid.. they will understand... and sometimes they will ask again... So such questions are asked. So this is a classic "gotcha" interview question. The interviewer is testing two things: your technical knowledge of energy markets and, more importantly, your attention to data validation.

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!

Kariyer ilerlemesinde alınan sertifikaların önemli olduğunu düşünüyor musunuz? Düşünüyorsanız hangi sertifikaları tavsiye edersiniz?

In Energy Economics/ Finance the CDF of the standard normal distribution is very popular. It appears in many economic models. If a model involves risk, probability, (like strike prices), the CDF is usually the tool doing the calculation. No need to know all the details e.g. mathematical formulas etc. We just need to interpret it. Also in our code we need to implement it. You can copy-paste it from this file (attached) into any model you develop that includes the CDF. Specifically this is the CDF of the standard normal distribution. CDF = Cumulative Distribution Function. The word "Cumulative" means "accumulating" or "summing up". Attached is a Python file (.ipynb for Jupyter notebook) that implements the Cumulative Distribution Function (CDF) for the standard normal distribution in Python, in two ways. The video explains all we need to know about the CDF for energy economics. Such a question is very frequent in interviews. E.g. to explain what the CDF is, and how to implement it in Python.