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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 Exercises50,533 Learners
3700 XP

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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

    Free

    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.

    Play Chapter Now
    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!

    Play Chapter Now
    Introduction to relational plots and subplots
    50 xp
    Creating subplots with col and row
    100 xp
    Creating two-factor subplots
    100 xp
    Customizing scatter plots
    50 xp
    Changing the size of scatter plot points
    100 xp
    Changing the style of scatter plot points
    100 xp
    Introduction to line plots
    50 xp
    Interpreting line plots
    100 xp
    Visualizing standard deviation with line plots
    100 xp
    Plotting subgroups in line plots
    100 xp
  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.

    Play Chapter Now
    Count plots and bar plots
    50 xp
    Count plots
    100 xp
    Bar plots with percentages
    100 xp
    Customizing bar plots
    100 xp
    Box plots
    50 xp
    Create and interpret a box plot
    100 xp
    Omitting outliers
    100 xp
    Adjusting the whiskers
    100 xp
    Point plots
    50 xp
    Customizing point plots
    100 xp
    Point plots with subgroups
    100 xp
  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!

    Play Chapter Now
    Changing plot style and color
    50 xp
    Changing style and palette
    100 xp
    Changing the scale
    100 xp
    Using a custom palette
    100 xp
    Adding titles and labels: Part 1
    50 xp
    FacetGrids vs. AxesSubplots
    100 xp
    Adding a title to a FacetGrid object
    100 xp
    Adding titles and labels: Part 2
    50 xp
    Adding a title and axis labels
    100 xp
    Rotating x-tick labels
    100 xp
    Putting it all together
    50 xp
    Box plot with subgroups
    100 xp
    Bar plot with subgroups and subplots
    100 xp
    Well done! What's next?
    50 xp

In the following tracks

Data Analyst Data Scientist Data Visualization

Datasets

CountriesMileage per gallonStudentsSurvey responses

Collaborators

Yashas RoyMona Khalil

Prerequisites

Introduction to Data Science in Python
Erin Case Headshot

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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