Delve into data visualization and analysis with Python's Seaborn library. Learn about variable types, data cleaning, creating detailed visualizations, and applying themes for more appealing results.

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jupyter.tar.gz

Adding Hue Parameter for color encoding

Data cleaning with Pandas

Getting Started with Seaborn

Intro to Pandas

Line Plots in Seaborn

Pair Plot

Scatter Plot

libraries

project

solution

This course aims to provide an introduction to data visualization and analysis using Python and the Seaborn library.

The course begins by introducing various variable types and statistical analysis methods. Then, you get to review the foundations of data cleaning and extraction using the pandas library. In the second half of the course, you will go over different plots in Seaborn for numerical, continuous, and categorical data, as well as distribution and regression plots to gain insightful information and identify patterns in the data. Lastly, you get to learn to create complex visualizations that are also aesthetically pleasing and go into great detail about the Seaborn themes, color palettes, styling, and multiplot grids.

By the end of this course, you’ll apply the knowledge you’ve gained with a hands-on project.

Data Visualization and Analysis With Seaborn Library

The seaborn library has five built-in themes to control and style figure aesthetics. The five built-in themes are as follows:
- Dark grid 
- White grid 
- White 
- Dark 
- Ticks 

By default, seaborn plots use the dark grid theme. We can change the default theme in seaborn using the `sns.set_style()` function by passing the desired theme name in the function. 



Data visualizations are essential for conveying interesting patterns and insights to an audience, making it necessary to have figures that grab the viewer's interest. The styling of the plot dramatically influences how the targeted audience will perceive the figure. Even though there's no hard and fast rule to use a specific theme, they're each suited to different applications and personal preferences. However, it may be better to choose themes in accordance with the end goals of your visualizations and targeted audience. We'll review all five themes separately and discuss use cases where they can be used.

Learn how to customize figure aesthetics using Seaborn's built-in themes.

About the Course

Introduction to Seaborn and Statistical Analysis

Plotting Numerical Data

Assessment on Plotting Numerical Data

Plotting Categorical Data

Plotting the Categorical Data

Assessment on Categorical Plots

Visualizing Distribution of Data

Data Visualization with Seaborn for Walmart Sales Projection

Assessment on Distributions of Data

Visualizing Regression Models

Assessment on Visualization of Regression Models

Styling and Figure Aesthetics

Assessment on Styling and Figure Aesthetics

Multiplot Grids

Assessment on Multi-Plot Grids

Project

Closing Remarks

Seaborn's Built-in Themes

Overview

Application of built-in themes