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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

Challenge 2: Exploring Categorical Data

Visualizing categorical data is crucial for gaining insights into how different categories relate to other variables. Categorical data, unlike continuous data, falls into discrete categories or labels. Seaborn, with its suite of powerful tools, provides efficient ways to visualize and interpret such data.

Visualizing categorical variables with Seaborn allows you to:

Compare the distribution of a numerical variable across different categories.
Visualize the relationships between two categorical variables.
Highlight how categorical variables relate to one or more numerical variables.

By leveraging Seaborn's functionalities, one can dive deep into the intricacies of categorical data, enabling a holistic understanding of its nuances.

Завдання

Using Seaborn, dive into the world of categorical data visualization:

Create a box plot to display the distribution of a numerical variable across different categories.
Display the distribution of a numerical variable across different categories using a swarm plot.
Visualize the count of observations in each category using a count plot.

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Секція 5. Розділ 2

Challenge 2: Exploring Categorical Data

Visualizing categorical variables with Seaborn allows you to:

Compare the distribution of a numerical variable across different categories.
Visualize the relationships between two categorical variables.
Highlight how categorical variables relate to one or more numerical variables.

By leveraging Seaborn's functionalities, one can dive deep into the intricacies of categorical data, enabling a holistic understanding of its nuances.

Завдання

Using Seaborn, dive into the world of categorical data visualization:

Create a box plot to display the distribution of a numerical variable across different categories.
Display the distribution of a numerical variable across different categories using a swarm plot.
Visualize the count of observations in each category using a count plot.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 5. Розділ 2

Challenge 2: Exploring Categorical Data

Visualizing categorical variables with Seaborn allows you to:

Compare the distribution of a numerical variable across different categories.
Visualize the relationships between two categorical variables.
Highlight how categorical variables relate to one or more numerical variables.

By leveraging Seaborn's functionalities, one can dive deep into the intricacies of categorical data, enabling a holistic understanding of its nuances.

Завдання

Using Seaborn, dive into the world of categorical data visualization:

Create a box plot to display the distribution of a numerical variable across different categories.
Display the distribution of a numerical variable across different categories using a swarm plot.
Visualize the count of observations in each category using a count plot.

Все було зрозуміло?

Дякуємо за ваш відгук!

Visualizing categorical variables with Seaborn allows you to:

Compare the distribution of a numerical variable across different categories.
Visualize the relationships between two categorical variables.
Highlight how categorical variables relate to one or more numerical variables.

By leveraging Seaborn's functionalities, one can dive deep into the intricacies of categorical data, enabling a holistic understanding of its nuances.

Завдання

Using Seaborn, dive into the world of categorical data visualization:

Create a box plot to display the distribution of a numerical variable across different categories.
Display the distribution of a numerical variable across different categories using a swarm plot.
Visualize the count of observations in each category using a count plot.

Секція 5. Розділ 2