Are you preparing for an interview as a data scientist? Then here’s what I got for you. In this article, I will be sharing with you some of the most frequently asked data science interview questions asked by top companies.

This is a really interesting topic to discuss. So let’s jump in.

#1

What will you do if removing missing values from a data set causes bias?

#2

Cars are implemented with a speed tracker so that insurance companies can track our driving state. Based on this new scheme what kind of business questions can be answered?

#3

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

#4

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?

#5

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

#6

If you could get the dataset on any topic of interest, irrespective of the collection methods or resources then how would the dataset look like and what will you do with it?

#7

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?

#8

You have a google app and you make a change. How do you test if a metric has increased or not?

#9

If the labels are known in the clustering project, how to evaluate the performance of the model?

#10

What is the difference between a bagged model and a boosted model?

#11

How is logistic regression done?

#12

Explain the steps in making a decision tree.

#13

How to check if the regression model fits the data well?

#14

Difference between convex and non-convex cost function; what does it mean when a cost function is non-convex?

#15

A box has 12 red cards and 12 black cards. Another box has 24 red cards and 24 black cards. You want to draw two cards at random from one of the two boxes, one card at a time. Which box has a higher probability of getting cards of the same color and why?

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Do let me know in the comments if you want the solution for these data science interview questions.

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