Python for Data Science: Basic Data Visualization Using Seaborn

Python    |    Intermediate
  • 11 videos | 1h 6m 18s
  • Includes Assessment
  • Earns a Badge
Rating 4.6 of 328 users Rating 4.6 of 328 users (328)
Explore Seaborn, a Python library used in data science that provides an interface for drawing graphs that conveys a lot of information, and are also visually appealing. To take this course, learners should be comfortable programming in Python and using Jupyter notebooks; familiarity with Pandas for Numpy would be helpful, but is not required. The course explores how Seaborn provides higher-level abstractions over Python's Matplotlib, how it is tightly integrated with the PyData stack, and how it integrates with other data structure libraries such as NumPy and Pandas. You will learn to visualize the distribution of a single column of data in a Pandas DataFrame by using histograms and the kernel density estimation curve, and then slowly begin to customize the aesthetics of the plot. Next, learn to visualize bivariate distributions, which are data with two variables in the same plot, and see the various ways to do it in Seaborn. Finally, you will explore different ways to generate regression plots in Seaborn.

WHAT YOU WILL LEARN

  • Describe what seaborn is and how it relates to other data science libraries in python
    Install seaborn and load a dataset for analysis
    Define and plot the distribution of a single variable using a histogram and kernel density estimate curve
    Configure an univariate distribution's appearance, including color, size, and the components of the plot
    Analyze the relationship between two variables by plotting a bivariate distribution
  • Distinguish between scatter plots, hexbin plots, and kde plots
    Use the seaborn pair plot to generate a grid to plot the relationship between multiple pairs of variables in your dataset
    Perform a regression analysis on a pair of variables in your dataset by using the seaborn lmplot
    Describe the basic aesthetic themes and styles available in seaborn
    Recall some of the use cases and features of seaborn

IN THIS COURSE

  • 2m 18s
  • 6m 28s
    Upon completion of this video, you will be able to describe what Seaborn is and how it relates to other data science libraries in Python. FREE ACCESS
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    3.  Install Seaborn
    6m 48s
    To install Seaborn, follow these instructions. To load a dataset for analysis, see this guide. FREE ACCESS
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    4.  Simple Univariate Distributions
    6m 41s
    In this video, you will learn how to define and plot the distribution of a single variable using a histogram and kernel density estimate curve. FREE ACCESS
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    5.  Configure Univariate Distribution Plots
    6m 28s
    In this video, you will learn how to configure an univariate distribution's appearance, including color, size, and the components of the plot. FREE ACCESS
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    6.  Simple Bivariate Distributions
    6m
    To analyze the relationship between two variables, plot a bivariate distribution. FREE ACCESS
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    7.  Explore Different Types of Bivariate Distributions
    5m 21s
    In this video, you will learn how to distinguish between scatter plots, hexbin plots, and KDE plots. FREE ACCESS
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    8.  Analyze Multiple Variable Pairs
    6m 29s
    In this video, you will learn how to use the Seaborn pair plot to generate a grid that plots the relationships between multiple pairs of variables in your dataset. FREE ACCESS
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    9.  Regression Plots
    7m 7s
    In this video, learn how to perform a regression analysis on a pair of variables in your dataset by using the Seaborn lmplot function. FREE ACCESS
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    10.  Themes and Styles in Seaborn
    7m 26s
    Upon completion of this video, you will be able to describe the basic aesthetic themes and styles available in Seaborn. FREE ACCESS
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    11.  Exercise: Basic Data Visualization Using Seaborn
    5m 12s
    After completing this video, you will be able to recall some of the uses and features of Seaborn. FREE ACCESS

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