# Python Statistical Plots: Visualizing & Analyzing Data Using Seaborn

Python    |    Intermediate
• 17 videos | 1h 46m 42s
• Includes Assessment
Rating 4.2 of 13 users (13)
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

• In this video, you will learn how to install the necessary Python modules to work with Seaborn.
• 3.  Visualizing Univariate Data
Learn how to create histograms for data with one variable.
• 4.  Representing Data Using Histograms
Learn how to use the distplot() function to customize histograms.
• 5.  Creating KDE Curves
In this video, find out how to create KDE curves at the figure level and the axis level.
• 6.  Creating Univariate Plots
Learn how to create bar charts, KDE curves, and rug plots.
• 7.  Representing Data Using Bivariate Visualizations
In this video, you will learn how to represent bivariate visualizations with color coding and grouped charts.
• 8.  Creating KDE Curves and Cumulative Distributions
In this video, you will learn how to create univariate KDE curves and cumulative distributions.
• 9.  Visualizing Data Using Bivariate Histograms
In this video, you will learn how to visualize bivariate histograms and KDE curves.
• 10.  Understanding and Implementing Joint Plots
In this video, you will learn how to customize joint plots using histograms, KDE curves, hexbin, and regression charts.
• 11.  Understanding and Implementing Scatter Plots
In this video, you will learn how to create figure-level and axis-level scatter plots.
• 12.  Customizing Scatter Plots with Multiple Variables
In this video, find out how to customize scatter plots with multiple variables and visualize categorical data.
• 13.  Creating Box Plots
Learn how to use the catplot and boxplot functions to create box plots and whisker plots.
• 14.  Understanding and Implementing Boxen Plots in Seaborn
In this video, you will compare and contrast box plots and boxen plots.
• 15.  Representing Data Using Violin Plots
Learn how to use the figure-level catplot() and the axis-level violinplot().
• 16.  Customizing Custom Violin Plots
In this video, you will learn how to customize violin plots using hue and bandwidth.
• 17.  Course Summary

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