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