An introductory course to familiarize the rich library of Seaborn to visualize variety of data types.

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

We can achieve this by specifying `x='gender'` and `y='participated'`.

We can achieve this by specifying `x='gender'` and `hue='participated'`.

We have a dataset containing information on students who have participated in debates.
The variable `participated` is a categorical variable containing two unique values: "Yes" represents the students who participated and "No" represents the students who did not participate. We have another variable, `gender`, to represent the gender of students. We're interested in visualizing the ratio of male to female students who participated in debates. How can we represent it using seaborn's `countplot()` function?

We have a dataset containing information on students who have participated in debates.
The variable <code>participated</code> is a categorical variable containing two unique values: “Yes” represents the students who participated and “No” represents the students who did not participate. We have another variable, <code>gender</code>, to represent the gender of students. We’re interested in visualizing the ratio of male to female students who participated in debates. How can we represent it using seaborn’s <code>countplot()</code> function?

We have a dataset of company employees containing salary information for various designations in various departments. We want to visualize the salary for each department so that we know each department's average salary. Which seaborn visualization is the suitable choice? 

We have a dataset of company employees containing salary information for various designations in various departments. We want to visualize the salary for each department so that we know each department’s average salary. Which seaborn visualization is the suitable choice?

We have a dataset of students studying in a school containing the family's annual income. However, the annual income is varied across different students of the school. Which seaborn visualization can we use to highlight the average annual income of students and those whose annual income is much higher than the rest? 

We have a dataset of students studying in a school containing the family’s annual income. However, the annual income is varied across different students of the school. Which seaborn visualization can we use to highlight the average annual income of students and those whose annual income is much higher than the rest?

A seaborn violin plot is a combination of ________.

A seaborn violin plot is a combination of ________.

datasets only containing numeric variables

datasets only containing categorical variables

Swarm plots are suitable for ________.

Quiz on seaborn's categorical plots

Quiz on seaborn’s categorical plots

Test yourself on applying seaborn categorical plots as per data needs.

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

Quiz on Plotting Categorical Data