Google Data Scientist interview questions with answers

Upasana | August 31, 2019 | 2 min read | 644 views

Source of questions only: Glassdoor, Medium

  1. Explain a probability distribution that is not normal and how to apply that

  2. Why use feature selection? If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression? What are the confidence intervals of the coefficients?

  3. K-mean and Gaussian mixture model: what is the difference between K-mean and EM? How to decided k?

  4. When using Gaussian mixture model, how do you know it is applicable? (Normal distribution)

  5. How can you tell if a given coin is biased?

  6. use of p-values in high dimensional linear regression.

  7. What is the derivative of 1/x?

  8. Draw the curve log(x+10)

  9. How to design a customer satisfaction survey?

  10. Tossing a coin ten times resulted in 8 heads and 2 tails. How would you analyze whether a coin is fair? What is the p-value?

  11. You have 10 coins. You toss each coin 10 times (100 tosses in total) and observe results. Would you modify your approach to the the way you test the fairness of coins?

  12. Why not logistic regression, why GBM?

  13. Derive the equations for GMM.

  14. How would you measure how much users liked videos?

  15. Simulate a bivariate normal

  16. Derive variance of a distribution

  17. How many people apply to Google per year?

  18. How do you build estimators for medians?

  19. If each of the two coefficient estimates in a regression model is statistically significant, do you expect the test of both together is still significant?

Top articles in this category:
  1. Top 100 interview questions on Data Science & Machine Learning
  2. Google Colab: import data from google drive as pandas dataframe
  3. Python - Get Google Analytics Data
  4. Flask Interview Questions
  5. AWS Lambda Interview Questions for Developers
  6. Python send GMAIL with attachment
  7. Python coding challenges for interviews

Recommended books for interview preparation:

Find more on this topic: