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.

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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

The default theme for seaborn plots is ________.

The default theme for seaborn plots is ________.

Let's say we need to implement a machine learning model. As a first step, we want to visualize the correlation among different features using a heatmap. Which seaborn color palette would be the appropriate choice given our data and end goal?

Let’s say we need to implement a machine learning model. As a first step, we want to visualize the correlation among different features using a heatmap. Which seaborn color palette would be the appropriate choice given our data and end goal?

We have a dataset containing a student's exam scores in different subjects such as mathematics, English, history, and so on, with a total of eight subjects. Which seaborn color palette would be suitable to represent this data in different visualizations so that we can see the students' scores along different subjects?

We have a dataset containing a student’s exam scores in different subjects such as mathematics, English, history, and so on, with a total of eight subjects. Which seaborn color palette would be suitable to represent this data in different visualizations so that we can see the students’ scores along different subjects?

We want to visualize the spread of COVID-19 in different years, and look at when the most cases were reported and how it spread within various countries. Which seaborn theme would be suitable to observe this data?

We want to visualize the spread of COVID-19 in different years, and look at when the most cases were reported and how it spread within various countries. Which seaborn theme would be suitable to observe this data?

Which property of a seaborn plot can be used to modify the transparency of seaborn axis spines?

Which property of a seaborn plot can be used to modify the transparency of seaborn axis spines?

Quiz on styling and figure aesthetics

Quiz on styling and figure aesthetics

Test yourself on the fundamentals of styling and figure aesthetics.

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 Styling and Figure Aesthetics