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.

Data Visualization and Analysis With Seaborn Library (1).png

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

**Seaborn** is a Python data visualization library built on top of the Matplotlib library. It provides a better user experience by being easy to use as well as offering various options to adjust the figure styling and aesthetics as needed. In addition, it's the most common library used for exploratory data analysis due to it being so easy to use and its support for various data formats.

In the figure below, we've analyzed the `tips` dataset that's available in the seaborn library. We can use the seaborn library to create many of these kinds of visualizations for in-depth data analysis. 

We can use and access the seaborn library on any Python-supported platform, such as Google Colab and Jupyter Notebook. We'll use the in-lesson Python code widgets to explore and understand the seaborn library.

The installation of the seaborn library can be done with the following command: `pip install seaborn`.

Learn how to use and access the seaborn library.

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

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

Getting Started with Seaborn Library

What is the seaborn library?

Seaborn installation and usage