The second question was trying to predict for 7 days. Follow up question was if u predict using the prediction u might accumulate errors.
Data Scientist Interviews
Data Scientist Interview Questions
In a data scientist interview, expect employers to ask questions that assess your data modeling, problem-solving, and programming skills. Be prepared to answer general questions that test your knowledge of statistics and data science. You should also be ready to answer open-ended questions that test your creativity, communication skills, and formal education in data modeling and programming.
Top Data Scientist Interview Questions & How to Answer
Question 1
Question #1: Which data modeling techniques do you prefer and why?
How to answer
How to answer: Turning data into understandable and actionable information is a critical part of the data scientist's job. This question allows employers to understand your data modeling skills and background. List and discuss your preferred data modeling techniques, including benefits such as ease of use, flexibility, etc.
Question 2
Question #2: How would you detect bogus Instagram accounts used for scamming consumers?
How to answer
How to answer: Questions like this one allow an employer to test your problem-solving skills. When answering open-ended questions such as these, feel free to ask clarifying questions and use whiteboards to demonstrate your coding and diagramming skills. Share your thought process as you work through the problem.
Question 3
Question #3: Describe circumstances that require a list, tuple, or set in Python.
How to answer
How to answer: Interviewers will use questions such as this one to test your Python programming skills. Review Python basics such as lists, tuples, and sets before your interview. You should be able to explain when and how each tool is used by data scientists.
54,212 data scientist interview questions shared by candidates
Tell me about the time you influenced someone
How would you make a product using this piece of paper?
If you could only pick one metric to measure a game's success, what would it be?
Difference between supervised and unsupervised machine learning ?
You're at a casino with two dice, if you roll a 5 you win, and get paid $10. What is your expected payout? If you play until you win (however long that takes) then stop, what is your expected payout?
Due to the NDA I had to sign, I will not mention key things. I was asked questions that tested my knowledge about SQL, business sense, and probability/math.
In Mexico, if you take the mean and the median age, which one will be higher and why?
If you draw 2 cards from a shuffled 52 card deck, what is the probability that you'll have a pair?
If the number of Facebook friend requests dropped by 10%, how would you investigate the problem as a data scientist?
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