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Introduction to Data Visualization with Seaborn

Learn how to create informative and attractive visualizations in Python using the Seaborn library.

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4 Hours14 Videos44 Exercises65,424 Learners3700 XPData Analyst TrackData Scientist TrackData Visualization Track

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Course Description

Seaborn is a powerful Python library that makes it easy to create informative and attractive visualizations. This course provides an introduction to Seaborn and teaches you how to visualize your data using plots such as scatter plots, box plots, and bar plots. You’ll do this while exploring survey responses about student hobbies and the factors that are associated with academic success. You’ll also learn about some of Seaborn’s advantages as a statistical visualization tool, such as how it automatically calculates confidence intervals. By the end of the course, you will be able to use Seaborn in a variety of situations to explore your data and effectively communicate the results of your data analyses to others.

  1. 1

    Introduction to Seaborn


    What is Seaborn, and when should you use it? In this chapter, you will find out! Plus, you will learn how to create scatter plots and count plots with both lists of data and pandas DataFrames. You will also be introduced to one of the big advantages of using Seaborn - the ability to easily add a third variable to your plots by using color to represent different subgroups.

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    Introduction to Seaborn
    50 xp
    Making a scatter plot with lists
    100 xp
    Making a count plot with a list
    100 xp
    Using pandas with Seaborn
    50 xp
    "Tidy" vs. "untidy" data
    100 xp
    Making a count plot with a DataFrame
    100 xp
    Adding a third variable with hue
    50 xp
    Hue and scatter plots
    100 xp
    Hue and count plots
    100 xp
  2. 2

    Visualizing Two Quantitative Variables

    In this chapter, you will create and customize plots that visualize the relationship between two quantitative variables. To do this, you will use scatter plots and line plots to explore how the level of air pollution in a city changes over the course of a day and how horsepower relates to fuel efficiency in cars. You will also see another big advantage of using Seaborn - the ability to easily create subplots in a single figure!

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  3. 3

    Visualizing a Categorical and a Quantitative Variable

    Categorical variables are present in nearly every dataset, but they are especially prominent in survey data. In this chapter, you will learn how to create and customize categorical plots such as box plots, bar plots, count plots, and point plots. Along the way, you will explore survey data from young people about their interests, students about their study habits, and adult men about their feelings about masculinity.

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  4. 4

    Customizing Seaborn Plots

    In this final chapter, you will learn how to add informative plot titles and axis labels, which are one of the most important parts of any data visualization! You will also learn how to customize the style of your visualizations in order to more quickly orient your audience to the key takeaways. Then, you will put everything you have learned together for the final exercises of the course!

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In the following tracks

Data Analyst Data Scientist Data Visualization


yashasYashas Roymona-kayMona Khalil
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Erin Case

Data Scientist

Erin is a Data Scientist who is passionate about both statistics and education. She enjoys experimental design, communicating data analyses to a wide range of audiences, and developing user-facing data products for technology companies. Previously, she was a biostatistician for two epidemiological studies on cardiac arrest.
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