Data Science Basics Interview Questions & Answers
Data Science Basics Interview Online Test
1. Describe the difference between supervised and unsupervised learning in data science
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2. What is overfitting in machine learning and how can it be prevented
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3. Explain the concept of cross-validation in machine learning
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4. What is the purpose of feature scaling and how is it performed
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5. Describe the difference between precision and recall in classification models
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6. What is a confusion matrix and what are its key components
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7. Explain the bias-variance tradeoff in machine learning
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8. What are ensemble methods and give examples
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9. Describe the purpose and method of dimensionality reduction in data science
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10. What is the role of the ROC curve and AUC in evaluating classification models
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11. Explain the difference between parametric and non-parametric models
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12. What is the purpose of regularization in machine learning
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13. Describe what a hyperparameter is and how it differs from a model parameter
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14. What are outliers and how can they impact a data analysis
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15. Explain the use of clustering in unsupervised learning
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16. What is the purpose of feature engineering in machine learning
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17. Describe the difference between a linear regression model and a logistic regression model
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18. What is the significance of the p-value in hypothesis testing
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19. How do you handle missing data in a dataset
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20. What is cross-validation and how does it improve model evaluation
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21. Explain the concept of time series analysis and its applications
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22. What is the purpose of data normalization and standardization
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23. Describe the difference between a decision tree and a random forest
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24. What are some common metrics for evaluating regression models
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25. How do you select important features for a machine learning model
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26. Explain the concept of ensemble learning and its benefits
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