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Intermediate Data Visualization with Seaborn

Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.

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4 Hours13 Videos50 Exercises56,992 Learners4200 XPData Scientist with Python TrackData Scientist Professional with Python Track

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Course Description

Do you want to make beautiful, informative visualizations with ease? If so, then you must learn seaborn! Seaborn is a visualization library that is an essential part of the python data science toolkit. In this course, you will learn how to use seaborn's sophisticated visualization tools to analyze multiple real world datasets including the American Housing Survey, college tuition data, and guests from the popular television series, The Daily Show. Following this course, you will be able to use seaborn functions to visualize your data in several different formats and customize seaborn plots for your unique needs.
  1. 1

    Seaborn Introduction

    Free

    Introduction to the Seaborn library and where it fits in the Python visualization landscape.

    Play Chapter Now
    Introduction to Seaborn
    50 xp
    Seaborn foundation
    50 xp
    Reading a csv file
    100 xp
    Comparing a histogram and displot
    100 xp
    Using the distribution plot
    50 xp
    Plot a histogram
    100 xp
    Rug plot and kde shading
    100 xp
    Interpreting the results
    50 xp
    Regression Plots in Seaborn
    50 xp
    Create a regression plot
    100 xp
    Plotting multiple variables
    100 xp
    Facetting multiple regressions
    100 xp
  2. 2

    Customizing Seaborn Plots

    Overview of functions for customizing the display of Seaborn plots.

    Play Chapter Now
    Using Seaborn Styles
    50 xp
    Setting the default style
    100 xp
    Comparing styles
    100 xp
    Removing spines
    100 xp
    Colors in Seaborn
    50 xp
    Matplotlib color codes
    100 xp
    Using default palettes
    100 xp
    Color Palettes
    50 xp
    Creating Custom Palettes
    100 xp
    Customizing with matplotlib
    50 xp
    Using matplotlib axes
    100 xp
    Additional plot customizations
    100 xp
    Adding annotations
    100 xp
    Multiple plots
    100 xp
  3. 3

    Additional Plot Types

    Overview of more complex plot types included in Seaborn.

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    Categorical Plot Types
    50 xp
    stripplot() and swarmplot()
    100 xp
    boxplots, violinplots and boxenplots
    100 xp
    barplots, pointplots and countplots
    100 xp
    Regression Plots
    50 xp
    Regression and residual plots
    100 xp
    Regression plot parameters
    100 xp
    Binning data
    100 xp
    Matrix plots
    50 xp
    Creating heatmaps
    100 xp
    Customizing heatmaps
    100 xp
  4. 4

    Creating Plots on Data Aware Grids

    Using Seaborn to draw multiple plots in a single figure.

    Play Chapter Now
    Using FacetGrid, catplot and lmplot
    50 xp
    Building a FacetGrid
    100 xp
    Using a catplot
    100 xp
    Using a lmplot
    100 xp
    Using PairGrid and pairplot
    50 xp
    Building a PairGrid
    100 xp
    Using a pairplot
    100 xp
    Additional pairplots
    100 xp
    Using JointGrid and jointplot
    50 xp
    Building a JointGrid and jointplot
    100 xp
    Jointplots and regression
    100 xp
    Complex jointplots
    100 xp
    Selecting Seaborn Plots
    50 xp

In the following tracks

Data Scientist with PythonData Scientist Professional with Python

Datasets

US Housing and Urban Development FY 2018 Fair Market RentWashington DC Bike Share2018 College Scorecard TuitionDaily Show GuestsAutomobile Insurance Premiums2010 US School Improvement Grants

Collaborators

Kara Woo
Becca Robins
Sara Snell

Prerequisites

Data Manipulation with pandas
Chris Moffitt Headshot

Chris Moffitt

Creator of Practical Business Python

Chris is an active python user with over ten years of experience using python for everything from web development to system administration and most recently data science. He is the author of the popular blog Practical Business Python where he describes how to use python's data science tools to solve common business problems. He has degrees in Electrical Engineering and Computer Science from Vanderbilt University and an MBA from the University of Minnesota.
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