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

Multi-plot grids can be constructed using ________.

Multi-plot grids can be constructed using ________.

A grid of facets and no data points. The size of the grid depends on the number of distinct values in the variable passed to the `FacetGrid()` constructor.

A grid of facets and data points. The size of the grid depends on the number of distinct values in the variable passed to the `FacetGrid()` constructor.

What does initializing the `FacetGrid()` constructor create? 

What does initializing the <code>FacetGrid()</code> constructor create?

 **(True or False)** By default, the `PairGrid` class plots all the numeric and categorical variables present in the data.

(True or False) By default, the <code>PairGrid</code> class plots all the numeric and categorical variables present in the data.

We have defined a the custom function `custom_func()`.
```
custom_func():
   return 10
```
Can we use this function with seaborn's `FacetGrid` class?

We have defined a the custom function <code>custom_func()</code>.
<pre><code>custom_func():
 return 10
</code></pre>
Can we use this function with seaborn’s <code>FacetGrid</code> class?

In how many dimensions can we represent the data using seaborn's `FacetGrid` class?

In how many dimensions can we represent the data using seaborn’s <code>FacetGrid</code> class?

Quiz on multi-plot grids

Quiz on multi-plot grids

Test yourself on using seaborn's multi-plot grid to represent complex visualizations.

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 Multi-Plot Grids