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

 **(True or False)** By default, the pandas `fillna()` method modifies the original DataFrame.

(True or False) By default, the pandas <code>fillna()</code> method modifies the original DataFrame.

We can't use categorical data in the `pairplot()` function because it only plots numerical variables.

We can use categorical variables by specifying them in any of the `x_vars` and `y_vars` arguments.

We can use the `hue` property to represent categories.

**(Select all that apply.)** How can we represent categorical data in seaborn's `pairplot()` function?

(Select all that apply.) How can we represent categorical data in seaborn’s <code>pairplot()</code> function?

**(Select all that apply.)** Which plots can we create using seaborn's  `relplot()`?



(Select all that apply.) Which plots can we create using seaborn’s <code>relplot()</code>?

We can use seaborn's `heatmap()` function to represent the correlation between different variables. Which part of the heatmap always represents the highest correlation?

We can use seaborn’s <code>heatmap()</code> function to represent the correlation between different variables. Which part of the heatmap always represents the highest correlation?

We have a dataset containing the accuracies of different machine learning models at different threshold values (0.1–0.9). We're interested in visualizing how these accuracies change at the different threshold values. Which is the appropriate plot to help us do that? 

We have a dataset containing the accuracies of different machine learning models at different threshold values (0.1–0.9). We’re interested in visualizing how these accuracies change at the different threshold values. Which is the appropriate plot to help us do that?

Quiz on plotting numerical data

Quiz on plotting numerical data

Test your knowledge of using seaborn with pandas and numerical data.

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 Numerical Data