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Data Science Interview Challenge
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Data Science Interview Challenge

Data Science Interview Challenge

1. Python
IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions
2. NumPy
Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions
3. Pandas
Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions
4. Matplotlib
Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions
5. Seaborn
Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions
6. Statistics
Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions
7. Scikit-learn
Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical 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:

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

Завдання

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

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

Все було зрозуміло?

Секція 5. Розділ 2
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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:

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

Завдання

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

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

Все було зрозуміло?

Секція 5. Розділ 2
toggle bottom row

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:

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

Завдання

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

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

Все було зрозуміло?

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:

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

Секція 5. Розділ 2
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