• Online, Self-Paced
Course Description

Seaborn is a data visualization library used for data science that provides a high-level interface for drawing graphs. These graphs are able to convey a lot of information, while also being visually appealing. In this course you will 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.

Learning Objectives

Python for Data Science: Advanced Data Visualization Using Seaborn

  • Course Overview
  • 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

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.


If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.