Raw LLM Responses

Looks like programming is not something that the interviewee is interested in. Lack of passion. The result of the school that gives you some free time depends significantly on what you do in that free time. I don't think that this is an effect of ChatGPT or something else on the generation, it think it was like this before as well. Many people get the degrees and that (opportunity to get) education becomes useless for most of them. Though in the US I've seen students taking their school program much more seriously compared to other places, as if someone would kill them in the end if they fail. Personal motivation (and passion) is very important, the student has to like the subject that he is studying, otherwise the required level of progress is hard to acquire. As a data scientist he is probably just using pandas and numpy toad local csv files and feeds them to some api that does the heavy lifting under the hood and then uses libs like matplotlib and others to present the result. So he probably never really studied the language and never going to dig into the details beyond the frameworks, libs and api. He probably also uses something like jupyter notebook for his pythony things. The questions asked are valid though and at work he would likely to be required to write some processing in pure python, which obviously requires some good knowledge of the language. At his entry position he might play with some framework and feed it data, but occasionally might be given a task to write a script, that parses an XML, modifies a few fields in a particular way and then saves it as json. It might not require deep knowledge of the language(like the order of calls to super(), or why the addresses of 10s match while the addresses of 1000s don't), yet the person is expected to be fluent with the basics and capable to automate otherwise manual work with files. He would also need to know a bit of R - that's mandatory for a data scientist in my opinion. And also math, the more math the person knows and understands the better, specifically the probability theory. Python also has scikit-learn library with lots of details that a data scientist has to know really good, that lib would be his domain, and he would be require to quickly write some code with that lib on the fly to demonstrate some core concepts of the model before running the full learning thing. Eventually (if he lends the job) he would be required to know the language with all its complexity and in-depth details(and there will be tools to accelerate that learning).