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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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Descripción del curso

Create Your Own Seaborn Plots

Seaborn is a powerful Python library that makes it easy to create informative and attractive data visualizations. This 4-hour course provides an introduction to how you can use Seaborn to create a variety of plots, including scatter plots, count plots, bar plots, and box plots, and how you can customize your visualizations.

Turn Real Datasets into Custom Seaborn Visualizations

You’ll explore this library and create your Seaborn plots based on a variety of real-world data sets, including exploring how air pollution in a city changes through the day and looking at what young people like to do in their free time. This data will give you the opportunity to find out about Seaborn’s advantages first hand, including how you can easily create subplots in a single figure and how to automatically calculate confidence intervals.

Improve Your Data Communication Skills

By the end of this course, you’ll be able to use Seaborn in various situations to explore your data and effectively communicate the results of your data analysis to others. These skills are highly sought-after for data analysts, data scientists, and any other job that may involve creating data visualizations. If you’d like to continue your learning, this course is part of several tracks, including the Data Visualization track, where you can add more libraries and techniques to your skillset.
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  1. 1

    Introduction to Seaborn

    Gratuito

    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.

    Reproducir Capítulo Ahora
    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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En las siguientes pistas

Científico de datos asociado en PythonAnalista de datos con PythonVisualización de datos con Python

Colaboradores

Collaborator's avatar
Mona Khalil
Collaborator's avatar
Yashas Roy
DataCamp Content Creator

Course Instructor

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