Data Science Interview Challenge

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Data Science Interview Challenge

Data Science Interview Challenge

1. Python

Introduction Challenge 1: List Comprehension Challenge 2: String Manipulation Challenge 3: Dictionary Manipulation Challenge 4: Nested Loops Challenge 5: Classes Theoretical Questions

2. NumPy

Challenge 1: Array Creation Challenge 2: Array Manipulation Challenge 3: Statistical Insights Challenge 4: Handling Missing Values Challenge 5: Subarray Sorting Theoretical Questions

3. Pandas

Challenge 1: DataFrame Creation Challenge 2: Data Grouping Challenge 3: Indexing and MultiIndexing Challenge 4: Altering DataFrame Challenge 5: Iterating Over Data Theoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of Plotting Challenge 2: Other Graph Types Challenge 3: Subplots Challenge 4: Customizing Plots Challenge 5: Adding Text to Plots Theoretical Questions

5. Seaborn

Challenge 1: Visualizing Distributions Challenge 2: Exploring Categorical Data Challenge 3: Relational Plots Challenge 4: Regression Plots Challenge 5: Matrix Plots Theoretical Questions

6. Statistics

Challenge 1: Probabilities and Distributions Challenge 2: Bayes' Theorem Challenge 3: Hypothesis Testing Challenge 4: Confidence Intervals Challenge 5: Correlation Theoretical Questions

7. Scikit-learn

Challenge 1: Data Scaling Challenge 2: Basic Model Creation Challenge 3: Pipelines Challenge 4: Cross-validation Challenge 5: Hyperparameter Tuning Theoretical Questions

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?

How does the hue parameter in Seaborn functions enhance your visualizations?

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It changes the background hue of the plot.

It allows data points to be colored based on a categorical or numerical variable, providing an additional layer of information.

It adjusts the transparency of data points.

It sets the primary color theme of the plot.

In Seaborn's displot(), what happens if you set the kde parameter to True?

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The histogram bars get colored according to data density.

The plot switches from vertical bars to horizontal bars.

A Kernel Density Estimate is plotted, giving a smoothed curve representation of the distribution.

The distribution becomes discrete instead of continuous.

When visualizing correlations between multiple variables in a dataset, which Seaborn function might you use?

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sns.heatmap()

sns.scatterplot()

sns.boxplot()

sns.catplot()

Which Seaborn function would you use to visualize the central tendency of a distribution with the spread of the data?

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sns.kdeplot()

sns.pairplot()

sns.lineplot()

sns.boxplot()

What is the primary difference between sns.lmplot() and sns.regplot()?

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sns.lmplot() does not support regression lines.

sns.regplot() is exclusively for non-linear regressions.

sns.lmplot() is a figure-level function that can create facetted plots, while sns.regplot() is an axes-level function that creates a single scatter plot.

sns.lmplot() is an older function, and sns.regplot() is its newer version.

Which of the following Seaborn functions is specifically designed to show pairwise differences in a categorical variable distribution using scatter or line plots?

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sns.swarmplot()

sns.catplot()

sns.jointplot()

sns.pointplot()

How does Seaborn relate to Matplotlib?

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Seaborn is an older version of Matplotlib.

Matplotlib is a sub-module of Seaborn used for basic plotting.

Seaborn and Matplotlib are competitors with no relationship.

Seaborn is built on top of Matplotlib and provides a higher-level interface to create statistical graphics.

In what scenario might you choose Seaborn over Matplotlib for your visualizations?

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When you require a 3D plot.

When you need a more advanced statistical visualization without having to code it from scratch.

When you're building a GUI application.

When you need to make a basic line or scatter plot.

When dealing with a large dataset, which library is generally more performance optimized?

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Matplotlib

Seaborn

Both perform identically.

It depends on the type of visualization.

Why might one use Matplotlib directly when working with Seaborn?

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Because Seaborn does not support scatter plots.

To customize or tweak specific aspects of a Seaborn plot that Seaborn's high-level interface might not expose.

To handle data preprocessing before visualization.

Seaborn does not support color customization.

How can you adjust the style of a Seaborn plot?

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By manipulating the data before plotting.

By importing a separate CSS file.

By using the sns.adjust() method.

By using the sns.set_style() function.

Which statement is true regarding the customization and flexibility of Matplotlib and Seaborn?

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Seaborn allows for more detailed customization than Matplotlib.

Matplotlib provides a more flexible canvas for detailed customization, whereas Seaborn offers pre-styled themes for ease of use.

Matplotlib is for basic plotting, while Seaborn is exclusively for complex charts.

Customization levels are identical in both libraries.

War alles klar?

Wie können wir es verbessern?

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Abschnitt 5. Kapitel 6

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