SkillSoft Explore Course – Lambers, Inc.

IT Skills Data and Databases Data Science Python for Data Science

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.

Asset ID	it_dspydsdj_06_enus
Course Type	Video Course
Course Category	Data and Databases

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

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