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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

book
Challenge 5: Matrix Plots

Data often comes in a matrix format, where rows and columns represent different variables or categories. To visualize this structured data effectively, matrix plots come to the rescue. Seaborn, known for its comprehensive plotting capabilities, offers specialized tools for creating powerful matrix visualizations.

Matrix plots in Seaborn allow you to:

  • Visualize the relationship between two categorical variables.
  • Display the distribution of data in a heatmap format.
  • Explore hierarchical structures in the data using cluster maps.

Leveraging Seaborn's matrix plots, analysts can navigate intricate data structures, extract patterns, and make data-driven decisions with ease.

Opgave

Swipe to start coding

Using Seaborn, demonstrate the matrix structure in a dataset:

  1. Plot a heatmap of a correlation matrix.
  2. Annotate the heatmap with the actual correlation values.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 5. Kapitel 5
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book
Challenge 5: Matrix Plots

Data often comes in a matrix format, where rows and columns represent different variables or categories. To visualize this structured data effectively, matrix plots come to the rescue. Seaborn, known for its comprehensive plotting capabilities, offers specialized tools for creating powerful matrix visualizations.

Matrix plots in Seaborn allow you to:

  • Visualize the relationship between two categorical variables.
  • Display the distribution of data in a heatmap format.
  • Explore hierarchical structures in the data using cluster maps.

Leveraging Seaborn's matrix plots, analysts can navigate intricate data structures, extract patterns, and make data-driven decisions with ease.

Opgave

Swipe to start coding

Using Seaborn, demonstrate the matrix structure in a dataset:

  1. Plot a heatmap of a correlation matrix.
  2. Annotate the heatmap with the actual correlation values.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 5. Kapitel 5
Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
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