Python for Data Science: Advanced Data Visualization Using Seaborn


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore how to analyze continuous and categorical variables in a dataset using various plotting options in Seaborn. These include box and violin plots, FacetGrids, and aesthetic elements such as color palettes.



Expected Duration (hours)
1.1

Lesson Objectives

Python for Data Science: Advanced Data Visualization Using Seaborn

  • 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
  • Course Number:
    it_dspydsdj_06_enus

    Expertise Level
    Intermediate