Course Content

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 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.

Task

Swipe to show code editor

Using Seaborn, demonstrate the matrix structure in a dataset:

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 5. Chapter 5

Challenge 5: Matrix Plots

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.

Task

Swipe to show code editor

Using Seaborn, demonstrate the matrix structure in a dataset:

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 5. Chapter 5

Challenge 5: Matrix Plots

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.

Task

Swipe to show code editor

Using Seaborn, demonstrate the matrix structure in a dataset:

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

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.

Task

Swipe to show code editor

Using Seaborn, demonstrate the matrix structure in a dataset:

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

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 5. Chapter 5

Switch to desktop for real-world practiceContinue from where you are using one of the options below