Top 7 Data Science Interview Questions

This article was published as a part of the Data Science Blogathon.

Introduction

Job interviews in data science demand particular abilities. The candidates who succeed in landing employment are often not the ones with the best technical abilities but those who can pair such capabilities with interview acumen.

Even though the field of data science is diverse, a few particular questions are frequently asked in interviews. Consequently, I have compiled a list of the seven most typical data science interview questions and their responses. Now, let’s dive right in!

Source: Glassdooor

Questions and Answers

Question 1: What assumptions are necessary for linear regression? What happens when some of these assumptions are violated?

Question 2: What does collinearity mean? What is multicollinearity? How do you tackle it? Does it have an impact on decision trees?

Question 3: How exactly does K-Nearest Neighbor work?

Question 4: What does the word “naive” refer to in Naive Bayes?

Question 5: When and why would you choose random forests over SVM?

Question 6: What distinguishes a Gradient Boosted tree from an AdaBoosted tree?

Question 7: How does the bias-variance tradeoff work?

Conclusion

In this article, we covered seven data science interview questions, and the following are the key takeaways:

1. Four necessary assumptions for the linear regression model includes: linearity, homoscedasticity, independence, and normality.

2. A linear relationship between two predictors is called collinearity, and Multi-collinearity refers to the relationship between two or more predictors in a regression model that is strongly linearly related.

3. K-Nearest Neighbors is a technique through which we can classify where a new sample is classified by looking at the nearest classified points, hence the name ‘K-nearest.’

4. Naive Bayes is naive since it makes this strong assumption since the features are presumed to be uncorrelated with one another, which is often never the case.

5. A random forest is a superior method to a support vector machine because Random forests allow us to determine the feature’s importance. SVMs are unable to achieve this.

6. The difference between an estimator’s true and expected values is called bias. High-bias models are often oversimplified, which leads to underfitting. The model’s sensitivity to the data and noise is represented by variance. Overfitting happens with high variance models.

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