7 Questions You Can Expect in Data Science Interview

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

         Source: DDI

Introduction

Data science job interviews need special skills. 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.

Although data science is broad, a few specific questions often come up in interviews. I have created a list of the seven most commonly-asked data science interview questions and their answers. 

Data Science Interview Questions

Question 1: How does XGBoost handle the bias-variance tradeoff?

Question 2: You must use multiple regression models to create a predictive model. Describe how you aim to validate this model.

Question 3: What distinguishes batch learning from online learning?

Question 4: Suggest some strategies for handling null values.

Question 5: Is it appropriate to impute mean values for missing data? Whether or not.

Question 6: How do you detect outliers?

Question 7: Is it appropriate to impute mean values for missing data? Why or why not?

Conclusion

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

• XGBoost is a boosted version of bagging and boosting. As a result, XGBoost manages bias and variance like any other boosting strategy. On the other hand, boosting is an ensemble meta-algorithm that takes a weighted average of different weak models to decrease bias and variation.
• Adjusted R-squared and Cross-validation can be used to validate a predictive model created using multiple regression models.
• When a model learns over groups of patterns, this process is called batch learning or offline learning. On the other hand, online learning uses an approach that ingests data one observation at a time.
• Z-score/standard deviations and Interquartile Range (IQR) can be used to check if there are outliers.

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