Python for Data Science: Advanced Data Visualization Using Seaborn

Python 3 | Intermediate

11 Videos | 1h 8m 19s
Includes Assessment
Earns a Badge

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

1.

Course Overview

2m 17s

UP NEXT
2.

Searching for Patterns in a Dataset

8m 3s
3.

Configuring Plot Aesthetics

6m 28s

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4.

Normal Distribution and Outliers

2m 19s

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5.

Distributions Within Categories - Part 1

8m

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6.

Distributions Within Categories - Part 2

5m 29s

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7.

Analyzing Categories with Facet Grids - Part 1

5m 13s

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8.

Analyzing Categories with Facet Grids - Part 2

7m 24s

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9.

Introducing Color Palettes

5m 12s

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10.

Using Color Palettes

7m 33s

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11.

Exercise: Advanced Data Visualization Using Seaborn

5m 52s

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EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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