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Data Science Interview Challenge
Data Science Interview Challenge
Theoretical Questions
1. How does the `hue` parameter in Seaborn functions enhance your visualizations?
2. In Seaborn's `displot()`, what happens if you set the `kde` parameter to `True`?
3. When visualizing correlations between multiple variables in a dataset, which Seaborn function might you use?
4. Which Seaborn function would you use to visualize the central tendency of a distribution with the spread of the data?
5. What is the primary difference between `sns.lmplot()` and `sns.regplot()`?
6. Which of the following Seaborn functions is specifically designed to show pairwise differences in a categorical variable distribution using scatter or line plots?
7. How does Seaborn relate to Matplotlib?
8. In what scenario might you choose Seaborn over Matplotlib for your visualizations?
9. When dealing with a large dataset, which library is generally more performance optimized?
10. Why might one use Matplotlib directly when working with Seaborn?
11. How can you adjust the style of a Seaborn plot?
12. Which statement is true regarding the customization and flexibility of Matplotlib and Seaborn?
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