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. Which statement best describes the purpose of a `colormap` in Matplotlib?

2. What is the primary purpose of using subplots in a figure?

3. Why is it important to label axes and provide legends in plots?

4. Which of the following is NOT a typical use case for Matplotlib?

5. How does Matplotlib handle the integration with Pandas DataFrames?

6. What is the significance of aspect ratio in a plot?

7. Which of the following statements about annotations in plots is true?

8. What does the following code do?

9. If you want to fill the area between two horizontal curves, which function can you use?

Which statement best describes the purpose of a colormap in Matplotlib?

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A sequence of colors used to map normalized data values to colors.

A tool for merging multiple plots into one.

A specific type of plot showing geographical information.

A method for identifying outliers in data.

What is the primary purpose of using subplots in a figure?

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To speed up the rendering time of a plot.

To display multiple plots in a single figure, organized in a grid.

To combine 2D and 3D plots into one.

To export multiple figures simultaneously.

Why is it important to label axes and provide legends in plots?

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To make the plot look more colorful.

To increase the rendering time of the plot.

To save the plot in higher resolution.

To improve the clarity and comprehensibility of the visualized data.

Which of the following is NOT a typical use case for Matplotlib?

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Visualizing time series data.

Displaying relationships between two variables.

Building interactive web applications.

Comparing distributions of data.

How does Matplotlib handle the integration with Pandas DataFrames?

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By transforming DataFrames to JSON before plotting.

By allowing direct plotting of data from DataFrame structures.

It doesn't support Pandas DataFrames.

What is the significance of aspect ratio in a plot?

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It is the ratio of 2D to 3D elements in a plot.

It refers to the ratio of missing data in a plot.

It defines the color contrast of a plot.

It determines the relationship between the width and height of the plot.

Which of the following statements about annotations in plots is true?

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Annotations provide additional information about specific data points or regions in the plot.

Annotations are only possible in 3D plots.

Annotations slow down the rendering time significantly.

Every plot must contain at least one annotation.

What does the following code do?

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Sets the axes to be logarithmic.

Removes the axes from the plot.

Zooms in on the plot.

Flips the x and y axes.

If you want to fill the area between two horizontal curves, which function can you use?

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plt.fill_between()

plt.area()

plt.shade()

plt.color_area()

War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 4. Kapitel 6

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