# Python Statistical Plots: Visualizing & Analyzing Data Using Seaborn

Python 3.8
• 17 Videos | 1h 54m 12s
• Includes Assessment
The wealth of Python data visualization libraries makes it hard to decide the best choice for each use case. However, if you're looking for statistical plots that are easy to build and visually appealing, Seaborn is the obvious choice. You'll begin this course by using Seaborn to construct simple univariate histograms and use kernel density estimation, or KDE, to visualize the probability distribution of your data. You'll then work with bivariate histograms and KDE curves. Next, you'll use box plots to concisely represent the median and the inter-quartile range (IQR) and define outliers in data. You'll work with boxen plots, which are conceptually similar to box plots but employ percentile markers rather than whiskers. Finally, you'll use Violin plots to represent the entire probability density function, obtained via a KDE estimation, for your data.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course install the necessary Python modules to work with Seaborn create histograms for univariate data use the distplot() function for customizing histograms create figure-level and axis-level KDE curves implement bar charts, KDE curves, and rug plots represent bivariate visualizations with color coding and grouped charts create univariate KDE curves and cumulative distributions visualize bivariate histograms and KDE curves
• customize joint plots using histograms, KDE curves, hexbin, and regression charts implement figure-level and axis-level scatter plots customize scatter plots with multiple variables and visualize categorical data use the catplot and boxplot functions to create box and whisker plots contrast box plots and boxen plots use the figure-level catplot() and axis-level violinplot() customize violin plots using hue and bandwidth summarize the key concepts covered in this course

## IN THIS COURSE

• 1.
Course Overview
• 2.
Installing the Seaborn Module
• 3.
Visualizing Univariate Data
• 4.
Representing Data Using Histograms
• 5.
Creating KDE Curves
• 6.
Creating Univariate Plots
• 7.
Representing Data Using Bivariate Visualizations
• 8.
Creating KDE Curves and Cumulative Distributions
• 9.
Visualizing Data Using Bivariate Histograms
• 10.
Understanding and Implementing Joint Plots
• 11.
Understanding and Implementing Scatter Plots
• 12.
Customizing Scatter Plots with Multiple Variables
• 13.
Creating Box Plots
• 14.
Understanding and Implementing Boxen Plots in Seaborn
• 15.
Representing Data Using Violin Plots
• 16.
Customizing Custom Violin Plots
• 17.
Course Summary

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