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Data Scientist Python Interview Questions
40,238 data scientist python interview questions shared by candidates
One technical one product analysis question.
Determime fraction of active users messagong at least 5 people in a given day.
What metric would you show small businesses if you were trying to have them sign up for Facebook Ads
Tell me about a time when you received constructive criticism and how you handled it.
If there's a new feature that attracts more pop stars to use it, how do you evaluate whether this feature is successful or not?
There is a building with 100 floors. You are given 2 identical eggs. How do you use 2 eggs to find the threshold floor, where the egg will definitely break from any floor above floor N, including floor N itself
PLEASE DON'T TAKE THE PHONE SCREENING LIGHTLY! I did and got rejected. I was expecting SQL questions and in general talk about my resume but she asked me a question on product sense and I was completely unprepared for it. Creation of Facebook user groups has gone down by 20%, what will you do? sounds simple but I messed it up so badly. I was just blabbering anything in an unstructured way, I sounded so stupid and not even fit for a small company forget Facebook. The recruiter was nice and she did not say anything but I were to hear my own answer, I would reject myself on spot. I regret it so much wish I could have prepared for it. I hope someone sees this and it helps them. The SQL questions were easy and I did answer them correctly- what kind of joins to get only common rows, what the natural sorting order etc.
SQL question that involved window functions
We have two types of reviewers: careful reviewer (80% of reviewers) and lazy reviewers (20% of reviewers). Careful reviewers rate a post positive 60% of time and negative 40% of time). Lazy reviewers however rate a post positive 100% of time. A) what is the probability that a random ad is reviewed positively? B) If an ad gets a negative review, what is the probability that it's reviewed by a lazy reviewer? C) If 3 ads are reviewed positively in a row, what is the probability that they are reviewed by a lazy reviewer? D) Some as above with n positively reviewed ads in a row. What happens when n goes to infinity? E) If we have very few labeled data, how can we build a model to distinguish between careful and lazy reviewers?
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