Python for Data Science: Advanced Data Visualization Using Seaborn

Python 3
  • 11 Videos | 1h 8m 19s
  • Includes Assessment
  • Earns a Badge
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Explore Seaborn, a Python library used in data science that provides an interface for drawing graphs that convey a lot of information, and are also visually appealing. To take this course, learners should be comfortable programming in Python, have some experience using Seaborn for basic plots and visualizations, and should be familiar with plotting distributions, as well as simple regression plots. You will work with continuous variables to modify plots, and to put it into a context that can be shared. Next, learn how to plot categorical variables by using box plots, violin plots, swarm plots, and FacetGrids (lattice or trellis plotting). You will learn to plot a grid of graphs for each category of your data. Learners will explore Seaborn standard aesthetic configurations, including the color palette, and style elements. Finally, this course teaches learners how to tweak displayed data to convey more information from the graphs.

WHAT YOU WILL LEARN

  • work with Seaborn to glean patterns in a dataset by visualizing the relationships between several pairs of variables
    define the aesthetic parameters for a plot and make use of Seaborn's built-in templates for creating shareable graphs
    recognize what a normal distribution is and what is defined as an outlier
    use boxplots and violin plots to visualize the distributions of data within specific categories of your dataset
    compare the use cases for swarm plots, bar plots strip plots, and categorical plots
  • create a FacetGrid to visualize distributions within a range of categories
    configure a FacetGrid to convey more information and to draw one's focus to specific plots
    describe what a color palette is and select from the built-in color palettes available
    identify the kinds of color palettes to use depending on the type of data it will represent
    recall different ways to visualize data within categories and identify use cases for specific aesthetic parameters

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 17s
    UP NEXT
  • Playable
    2. 
    Searching for Patterns in a Dataset
    8m 3s
  • Locked
    3. 
    Configuring Plot Aesthetics
    6m 28s
  • Locked
    4. 
    Normal Distribution and Outliers
    2m 19s
  • Locked
    5. 
    Distributions Within Categories - Part 1
    8m
  • Locked
    6. 
    Distributions Within Categories - Part 2
    5m 29s
  • Locked
    7. 
    Analyzing Categories with Facet Grids - Part 1
    5m 13s
  • Locked
    8. 
    Analyzing Categories with Facet Grids - Part 2
    7m 24s
  • Locked
    9. 
    Introducing Color Palettes
    5m 12s
  • Locked
    10. 
    Using Color Palettes
    7m 33s
  • Locked
    11. 
    Exercise: Advanced Data Visualization Using Seaborn
    5m 52s

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