Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Challenge 3: Relational Plots | Seaborn
Data Science Interview Challenge
course content

Course Content

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
2. NumPy
3. Pandas
4. Matplotlib
5. Seaborn
6. Statistics
7. Scikit-learn

bookChallenge 3: Relational Plots

Understanding relationships between variables is essential in data analysis. A robust way to visualize these relationships is through relational plots. Seaborn, with its intricate API, provides an array of tools to showcase how variables interact with one another.

Relational plots in Seaborn can:

  • Identify patterns, correlations, and outliers among two variables.
  • Present the relationship between multiple variables across complex datasets.
  • Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Task
test

Swipe to show code editor

Using Seaborn, visualize the relationships in a dataset:

  1. Create a line plot to track changes in a variable over time or sequential order.
  2. Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 3
toggle bottom row

bookChallenge 3: Relational Plots

Understanding relationships between variables is essential in data analysis. A robust way to visualize these relationships is through relational plots. Seaborn, with its intricate API, provides an array of tools to showcase how variables interact with one another.

Relational plots in Seaborn can:

  • Identify patterns, correlations, and outliers among two variables.
  • Present the relationship between multiple variables across complex datasets.
  • Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Task
test

Swipe to show code editor

Using Seaborn, visualize the relationships in a dataset:

  1. Create a line plot to track changes in a variable over time or sequential order.
  2. Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 3
toggle bottom row

bookChallenge 3: Relational Plots

Understanding relationships between variables is essential in data analysis. A robust way to visualize these relationships is through relational plots. Seaborn, with its intricate API, provides an array of tools to showcase how variables interact with one another.

Relational plots in Seaborn can:

  • Identify patterns, correlations, and outliers among two variables.
  • Present the relationship between multiple variables across complex datasets.
  • Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Task
test

Swipe to show code editor

Using Seaborn, visualize the relationships in a dataset:

  1. Create a line plot to track changes in a variable over time or sequential order.
  2. Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Understanding relationships between variables is essential in data analysis. A robust way to visualize these relationships is through relational plots. Seaborn, with its intricate API, provides an array of tools to showcase how variables interact with one another.

Relational plots in Seaborn can:

  • Identify patterns, correlations, and outliers among two variables.
  • Present the relationship between multiple variables across complex datasets.
  • Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Task
test

Swipe to show code editor

Using Seaborn, visualize the relationships in a dataset:

  1. Create a line plot to track changes in a variable over time or sequential order.
  2. Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 3
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
some-alt