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

We want to find the probability of whether a student will pass or fail their exam, given certain factors. Which regression model can be used to achieve this? 

We want to find the probability of whether a student will pass or fail their exam, given certain factors. Which regression model can be used to achieve this?

We can increase the degree of the polynomial using the `order` parameter.

We have plotted a `regplot()` of our data. The regression line doesn't fit on most of the data points. How can we overcome this problem?

We have plotted a <code>regplot()</code> of our data. The regression line doesn’t fit on most of the data points. How can we overcome this problem?

We have plotted a `regplot()` of our data. The regression line fits most of the data point, aside from a few. How can we overcome this problem?

We have plotted a <code>regplot()</code> of our data. The regression line fits most of the data point, aside from a few. How can we overcome this problem?

**(True or False)** To visualize the relationship of variables  along different rows and columns of the `regplot()`, we can use the `row` and `col` property of the `regplot()`.

(True or False) To visualize the relationship of variables along different rows and columns of the <code>regplot()</code>, we can use the <code>row</code> and <code>col</code> property of the <code>regplot()</code>.

We have a dataset of a tech company that contains their employees' years of experience and their annual salary. We observe that, as the employee gets more experienced, we see an increase in their salary. Which regression model is suitable for visualizing this data? 

We have a dataset of a tech company that contains their employees’ years of experience and their annual salary. We observe that, as the employee gets more experienced, we see an increase in their salary. Which regression model is suitable for visualizing this data?

Quiz on regression models

Quiz on regression models

Test your knowledge of using seaborn regression models to analyze 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 Regression Models