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Data Visualization and Analysis With Seaborn Library

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.

5.0

50 Lessons

12h

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LEARNING OBJECTIVES

The ability to perform data analysis using various statistical techniques
The ability to use Pandas library for data extraction and cleaning
The ability to use Seaborn plots for different data types
The ability to use Seaborn’s distribution and regression plots to extract and visualize interesting trends in data
The ability to use Seaborn's built-in themes, palettes, and grids to enhance the plot aesthetics

Learning Roadmap

50 Lessons7 Quizzes6 Assessments

About the Course

Get familiar with essential data visualization concepts, plotting styles, and seaborn's color palettes.

Introduction

Introduction to Seaborn and Statistical Analysis

Look at data visualization essentials, variable types, statistical analysis, and starting with Seaborn.

Overview of Data Visualization, Variables, and their Types

Types of Variables in Statistical Analysis

Types of Statistical Analysis

Getting Started with Seaborn Library

Quiz on Variable Concepts

Plotting Numerical Data

9 Lessons

Examine tools to manipulate, clean, and visualize numerical data using pandas and Seaborn.

Assessment on Plotting Numerical Data

Assessment

Plotting Categorical Data

Apply your skills to visualize and analyze categorical data using count plots in Seaborn.

Visualization with Count Plots

Plotting the Categorical Data

8 Lessons

Take a closer look at plotting categorical data using Seaborn's bar, point, box, violin, swarm, strip, and cat plots.

Assessment on Categorical Plots

Assessment

Visualizing Distribution of Data

7 Lessons

Investigate visualizing data distributions with KDE, hist, ECDF, rug, joint, and displots in Seaborn.

Data Visualization with Seaborn for Walmart Sales Projection

Project

Assessment on Distributions of Data

Assessment

Visualizing Regression Models

4 Lessons

Build on visualizing and analyzing regression models, reg plots, and lmplots with Seaborn.

Assessment on Visualization of Regression Models

Assessment

Styling and Figure Aesthetics

6 Lessons

Get familiar with figure aesthetics, Seaborn themes, scaling, styling, axis spines, and color palettes.

Assessment on Styling and Figure Aesthetics

Assessment

Multiplot Grids

6 Lessons

Walk through multi-plot grids to visualize variable relationships and customize plots.

Assessment on Multi-Plot Grids

Assessment

10.

Project

2 Lessons

11.

Closing Remarks

Grasp the fundamentals of data visualization and analysis using the Seaborn library.

Conclusion

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

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.

ABOUT THE AUTHOR

Mohammad Amir Asim Khan Jalwana

I am a dedicated computer scientist passionate about learning and teaching.

Learn more about Mohammad

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