Delve into data visualization and analysis with Python's Seaborn library. Gain insights into variable types, data cleaning, creating detailed visualizations, and applying themes for aesthetically pleasing 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

In this course, we'll cover all the essential aspects of data visualization. We'll begin by discussing the most fundamental concepts for data cleaning, the several plot styles appropriate for the different types of data, and univariate, bivariate, and multivariate analysis. We'll conclude our discussions on how to make our plots aesthetically pleasing by discussing style and aesthetics using a variety of seaborn color palettes and themes.

This is an introductory course for the seaborn library. It can be taken by anyone who is interested in data visualization and has basic programming knowledge. 

To be successful in this course, you should have a basic understanding of the Python programming language.

Get started with data analysis and visualization with 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

Introduction

Outline

Target audience

Prerequisites