Data Science Interview Challenge

Data Science Interview Challenge

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1. Python

Let's take a look at what we'll be working with in this course. The first section will acquaint you with Python, a flexible and advanced programming language known for its clear syntax and readability.

Challenge 1: List Comprehension

Challenge 2: String Manipulation

Challenge 3: Dictionary Manipulation

Challenge 4: Nested Loops

Challenge 5: Classes

Theoretical Questions

2. NumPy

NumPy is a fundamental library in Python that facilitates efficient numerical computations with powerful n-dimensional arrays and mathematical functions.

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

Pandas provides intuitive and versatile data structures for efficient data manipulation and analysis, streamlining the initial stages of the data science pipeline.

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

Matplotlib is a comprehensive Python library for creating static, animated, and interactive visualizations in Python.

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

Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.

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

Statistics provides data scientists with foundational techniques and tools to extract meaningful insights from data, allowing them to make informed decisions and predictions based on empirical evidence.

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

Scikit-learn is an open-source Python library that provides simple and efficient tools for data analysis and modeling, particularly for machine learning. Data scientists use it extensively for its comprehensive collection of algorithms and processing techniques, enabling them to quickly develop and deploy predictive models.

Challenge 1: Data Scaling

Challenge 2: Basic Model Creation

Challenge 3: Pipelines

Challenge 4: Cross-validation

Challenge 5: Hyperparameter Tuning

Theoretical Questions

Course Content

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?

Select the correct answer

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?

Select the correct answer

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?

Select the correct answer

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?

Select the correct answer

sns.kdeplot()

sns.pairplot()

sns.lineplot()

sns.boxplot()

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

Select the correct answer

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?

Select the correct answer

sns.swarmplot()

sns.catplot()

sns.jointplot()

sns.pointplot()

How does Seaborn relate to Matplotlib?

Select the correct answer

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?

Select the correct answer

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?

Select the correct answer

Matplotlib

Seaborn

Both perform identically.

It depends on the type of visualization.

Why might one use Matplotlib directly when working with Seaborn?

Select the correct answer

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?

Select the correct answer

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?

Select the correct answer

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.

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Section 5. Chapter 6

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