# Python for Data Science: Basic Data Visualization Using Seaborn

Python    |    Intermediate
• 11 videos | 1h 6m 18s
• Includes Assessment
Rating 4.6 of 328 users (328)
Explore Seaborn, a Python library used in data science that provides an interface for drawing graphs that conveys a lot of information, and are also visually appealing. To take this course, learners should be comfortable programming in Python and using Jupyter notebooks; familiarity with Pandas for Numpy would be helpful, but is not required. The course explores how Seaborn provides higher-level abstractions over Python's Matplotlib, how it is tightly integrated with the PyData stack, and how it integrates with other data structure libraries such as NumPy and Pandas. You will learn to visualize the distribution of a single column of data in a Pandas DataFrame by using histograms and the kernel density estimation curve, and then slowly begin to customize the aesthetics of the plot. Next, learn to visualize bivariate distributions, which are data with two variables in the same plot, and see the various ways to do it in Seaborn. Finally, you will explore different ways to generate regression plots in Seaborn.

## WHAT YOU WILL LEARN

• Describe what seaborn is and how it relates to other data science libraries in python
Install seaborn and load a dataset for analysis
Define and plot the distribution of a single variable using a histogram and kernel density estimate curve
Configure an univariate distribution's appearance, including color, size, and the components of the plot
Analyze the relationship between two variables by plotting a bivariate distribution
• Distinguish between scatter plots, hexbin plots, and kde plots
Use the seaborn pair plot to generate a grid to plot the relationship between multiple pairs of variables in your dataset
Perform a regression analysis on a pair of variables in your dataset by using the seaborn lmplot
Describe the basic aesthetic themes and styles available in seaborn
Recall some of the use cases and features of seaborn

## IN THIS COURSE

• Upon completion of this video, you will be able to describe what Seaborn is and how it relates to other data science libraries in Python.
• 3.  Install Seaborn
To install Seaborn, follow these instructions. To load a dataset for analysis, see this guide.
• 4.  Simple Univariate Distributions
In this video, you will learn how to define and plot the distribution of a single variable using a histogram and kernel density estimate curve.
• 5.  Configure Univariate Distribution Plots
In this video, you will learn how to configure an univariate distribution's appearance, including color, size, and the components of the plot.
• 6.  Simple Bivariate Distributions
To analyze the relationship between two variables, plot a bivariate distribution.
• 7.  Explore Different Types of Bivariate Distributions
In this video, you will learn how to distinguish between scatter plots, hexbin plots, and KDE plots.
• 8.  Analyze Multiple Variable Pairs
In this video, you will learn how to use the Seaborn pair plot to generate a grid that plots the relationships between multiple pairs of variables in your dataset.
• 9.  Regression Plots
In this video, learn how to perform a regression analysis on a pair of variables in your dataset by using the Seaborn lmplot function.
• 10.  Themes and Styles in Seaborn
Upon completion of this video, you will be able to describe the basic aesthetic themes and styles available in Seaborn.
• 11.  Exercise: Basic Data Visualization Using Seaborn
After completing this video, you will be able to recall some of the uses and features of Seaborn.

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