The signal to noise ratio is so awful these days that it is a serious liability to understand what you're talking about in data science interviews.
I have seen been on hiring side of multiple larger organizational interviews where the "answers" to the questions asked during interviews were not even correct. Candidates that knew what they were talking about would be rejected because clueless interviewer didn't know what they were talking about.
Whenever I have to interview I have to play guessing games with interviewers trying to understand what they think is the correct answer. I've had to explain that, yes you can do statistical inference with a linear model, that Poisson regression and Logistic regression are in fact only different in terms of a link function, and of course also had to explain why a Poisson random variable might help in modeling number of purchases. In all these cases the correct answer was "use XGBoost".
In my experience, anything more is dangerous.
The signal to noise ratio is so awful these days that it is a serious liability to understand what you're talking about in data science interviews.
I have seen been on hiring side of multiple larger organizational interviews where the "answers" to the questions asked during interviews were not even correct. Candidates that knew what they were talking about would be rejected because clueless interviewer didn't know what they were talking about.
Whenever I have to interview I have to play guessing games with interviewers trying to understand what they think is the correct answer. I've had to explain that, yes you can do statistical inference with a linear model, that Poisson regression and Logistic regression are in fact only different in terms of a link function, and of course also had to explain why a Poisson random variable might help in modeling number of purchases. In all these cases the correct answer was "use XGBoost".