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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
1 project
12h
Created by Mohammad Amir Asim Khan Jalwana
Data Visualization and Analysis With Seaborn Library
What's included
  • 50 lessons across 11 modules
  • 7 quizzes with instant feedback
  • 1 project in preconfigured environments
  • AI Tutor on every lesson
Who wrote it
Mohammad Amir Asim Khan Jalwana
I am a dedicated computer scientist passionate about learning and teaching.

I have had the privilege of gaining extensive academic and industrial experience, which has been instrumental in shaping my successful career. My unwavering dedication to excellence led me to pursue a Ph.D. at the esteemed University of Western Australia. During my studies, I focused on advancing the understanding of artificial intelligence models, culminating in a special mention on the Dean's list that recognizes exceptional academic achievement. At present, I am honored to serve as a Data Scientist at the renowned Fortescue Metal Group, where I apply my computer science skills to tackle complex problems that are intrinsic to the mining industry. Prior to this role, I dedicated myself to developing machine learning and artificial intelligence techniques for various ventures in Perth. Through my Post-Doctoral research, I had the opportunity to collaborate with the esteemed U.S. Department of Defense (DARPA) to develop cutting-edge algorithms that can deceive an unknown visual model. Additionally, my passion for programming languages is one of my defining attributes, and I continuously explore new avenues to develop and teach algorithms in Python, Java, C/C++, SQL, and Matlab.

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What You'll Learn

  • 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

2.

Introduction to Seaborn and Statistical Analysis

Introduction to Seaborn and Statistical Analysis

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

3.

Plotting Numerical Data

Plotting Numerical Data

9 Lessons

9 Lessons

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

5.

Plotting the Categorical Data

Plotting the Categorical Data

8 Lessons

8 Lessons

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

6.

Visualizing Distribution of Data

Visualizing Distribution of Data

7 Lessons

7 Lessons

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

7.

Visualizing Regression Models

Visualizing Regression Models

4 Lessons

4 Lessons

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

8.

Styling and Figure Aesthetics

Styling and Figure Aesthetics

6 Lessons

6 Lessons

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

9.

Multiplot Grids

Multiplot Grids

6 Lessons

6 Lessons

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

10.

Project

Project

2 Lessons

2 Lessons

Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Mohammad Amir Asim Khan JalwanaData Visualization and AnalysisWith Seaborn LibraryCourse Author
About this course
WHY I WROTE 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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