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

By increasing the bandwidth of the KDE plot

By decreasing the bandwidth of the KDE plot

By increasing the bandwidth and converting to a log axis

By decreasing the bandwidth and converting to a log axis

We've created a seaborn KDE distribution of exam scores. Currently, we see a smooth bell-shaped curve. However, we're interested in seeing more trends within the curve, such as the peaks and the lows. What can we do to achieve this?

We’ve created a seaborn KDE distribution of exam scores. Currently, we see a smooth bell-shaped curve. However, we’re interested in seeing more trends within the curve, such as the peaks and the lows. What can we do to achieve this?

A bivariate histogram is represented in the form of  ________.

A bivariate histogram is represented in the form of ________.

We're interested in visualizing the `salary` feature of the employee dataset so that its entire distribution is evident within the plot (the highs, the lows, the median). Which plot is the best choice for achieving this goal?

We’re interested in visualizing the <code>salary</code> feature of the employee dataset so that its entire distribution is evident within the plot (the highs, the lows, the median). Which plot is the best choice for achieving this goal?

**(True or False)** We can use the `col` property from seaborn's `jointplot()` function to represent categories along the columns of the plot.

(True or False) We can use the <code>col</code> property from seaborn’s <code>jointplot()</code> function to represent categories along the columns of the plot.

We're interested in visualizing the relationship between `tips` and `total_bill` in the `tips` dataset, along with the individual distribution of the `tips` and `total_bill` variables. Which seaborn visualization function is suitable for achieving this goal?

We’re interested in visualizing the relationship between <code>tips</code> and <code>total_bill</code> in the <code>tips</code> dataset, along with the individual distribution of the <code>tips</code> and <code>total_bill</code> variables. Which seaborn visualization function is suitable for achieving this goal?

Quiz on distribution plots

Quiz on distribution plots

Test yourself on the fundamentals of using seaborn distribution plots.

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 Distribution Plots