Complete Course on Data Visualization, Matpl…

Bekzod's instruction is very clear and concise. I went from having zero knowledge of Matplotlib to creating highly customized visualizations within hours. Prerequisites in Python and Pandas are not necessarily needed but understanding the basics in both will maximize your experience in this course. I recommend to open a blank notebook and following along with Bekzod, pausing along the way read the help documentation he references, as well as read any code snippets you may not understand right away. It takes a little longer to finish the course but it's more than worth it. I'm looking forward to additional courses offered by Bekzod." - Jeff Dowden

"I learn a lot from the lesson until now. This lesson improves my understanding of OOP. It is so easy, interesting and amazing to use python to visualize data from the perspective of OOP." - Haitao Lyu

"This course is completely amazing. Direct to the point and use real data not simulation with numpy as usually others did. Great job Bekzod. " - Hartanto

"'I've used Matplotlib and Seaborn for a number of years. I was reviewing this to see if it was a good introduction for people I work with. The answer, yes. It's a very good introduction that covers some of the critical details necessary to navigate Matplotlib in order to customize plots." - Stephen Basco

After completing this course you will master Matplotlib on an intuition level and feel comfortable visualizing and customizing Matplotlib, Seaborn and Pandas charts of any complexities. More specifically, this course is a great resource if you are interested in:

How Matplotlib Works
How to create charts from simple to scientific ones with Matplotlib, Pandas and Seaborn
How to customize charts of any complexities with ease

To achieve the objectives, I split this course into the following sections:

Matplotlib Anatomy

As the name implies, in this section you will learn how Matplotlib works and how a variety of charts are generated.

It gives you a solid understanding and a lot of aha-moments when it comes to creating and / or customizing charts that you haven't dealt with before.

Create 2D Charts

In this section, you will generate plethora of charts using Matplotlib OOP, and Pandas and mix them together to achieve the maximum efficiency and granular control over graphs.

Axes Statistical Charts

Here we will learn how to make statistical charts such as Auto Correlation, Boxplots, Violinplots and KDE plots with Matplotlib OOP and Pandas.

Seaborn

Seaborn, a high-level interface to Matplotlib helps make statistical plots with ease and charm. It is a must-know library for data exploration and super easy to learn. And in this section, we will create Regression plots, Count plots, Barplots, Factorplots, Jointplots, Boxplots, Violin plots and more.

Course Summary and Exercises

This section has dual purposes.

For one, it is a good summary of the course and provides you with exercises to test your knowledge and then provide solutions for comparison.

Secondly, If you are short-on time, you can start here and then move to other sections if you seek more granular coverage of the topic or when you have more time available.

Seaborn 0.8.1 or above

Pandas 0.22 or above

What's inside

Learning objectives

Learn matplotlib anatomy
Customize charts of any complexity with ease
Create a variety of charts, bar charts, line charts, stacked charts, donut and pie charts, histograms, kde plots, violinplots, boxplots, auto correlation plots, scatter plots, heatmaps
Feel comfortable managing various matplotlib artists such as legends, annotations, texts, patches, lines, collections, containers, axis

Create statistical charts with seaborn
Visualize data with matplotlib in oop
Dual axis charts

Learn matplotlib anatomy
Customize charts of any complexity with ease
Create a variety of charts, bar charts, line charts, stacked charts, donut and pie charts, histograms, kde plots, violinplots, boxplots, auto correlation plots, scatter plots, heatmaps
Feel comfortable managing various matplotlib artists such as legends, annotations, texts, patches, lines, collections, containers, axis
Create statistical charts with seaborn
Visualize data with matplotlib in oop
Dual axis charts

Syllabus

INTRODUCTION

Introduction

Instructor's message

Matplotlib Anatomy

You probably have gone through so many quizzes in your life and I don't want it to be one of them. I just have one question, if you don't mind.

Yup. This time more than one question ahead. But, I am sure you will get all the questions right, because you have watched the videos. I am not worried about you. It is the other guy I am worried about :)

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Delves deep into Matplotlib anatomy, fortifying your understanding of chart generation

Develops skills in Matplotlib, Pandas, and Seaborn to visualize data comprehensively

Teaches techniques to customize charts of any complexity, catering to specific visualization needs

Covers a wide range of chart types, including bar charts, line charts, histograms, and KDE plots, to meet diverse visualization requirements

Empowers learners with a thorough understanding of Matplotlib's functionalities, enabling them to create and modify charts with ease

Suitable for beginners seeking to establish a solid foundation in data visualization using Matplotlib

Reviews summary

Mastering python data visualization

According to students, this course offers a highly effective and comprehensive pathway to mastering data visualization in Python. Learners consistently highlight the instructor's clear and concise instruction, which enables them to rapidly progress from limited knowledge to creating highly customized visualizations. The course is praised for being direct to the point and utilizing real-world data, providing crucial practical experience. Many find the structured approach to Matplotlib anatomy and the strong emphasis on Object-Oriented Programming (OOP) for visualization particularly beneficial. It's considered an excellent introduction, even for those with prior experience in Matplotlib and Seaborn, and is well-suited for beginners to intermediate learners aiming for intuition-level mastery.

Basic knowledge in Python and Pandas enhances the learning experience.

"Prerequisites in Python and Pandas are not necessarily needed but understanding the basics in both will maximize your experience in this course."

"I recommend to open a blank notebook and following along with Bekzod, pausing along the way..."

"Having a foundational understanding of Python and Pandas certainly helped me get the most out of the course materials."

Suitable for beginners while still valuable for experienced users.

"I went from having zero knowledge of Matplotlib to creating highly customized visualizations within hours."

"'I've used Matplotlib and Seaborn for a number of years. I was reviewing this to see if it was a good introduction for people I work with. The answer, yes."

"As a beginner, I found the course easy to follow, and it laid a very strong foundation for my data visualization journey."

Offers thorough content, from basics to advanced customization.

"'I've used Matplotlib and Seaborn for a number of years... It's a very good introduction that covers some of the critical details necessary..."

"I learned a lot from the lesson until now. This lesson improves my understanding of OOP. It is so easy, interesting and amazing to use python to visualize data from the perspective of OOP."

"The comprehensive coverage, including Matplotlib anatomy and advanced customization, made me feel confident in my skills."

Course focuses on real data and practical, hands-on application.

"This course is completely amazing. Direct to the point and use real data not simulation with numpy as usually others did."

"I appreciated the focus on real data, which made the concepts immediately applicable to my own projects and analysis."

"The hands-on practice I gained allowed me to create highly customized visualizations within hours."

Instructor's teaching style is highly praised for clarity and conciseness.

"Bekzod's instruction is very clear and concise. I went from having zero knowledge of Matplotlib to creating highly customized visualizations..."

"I learn a lot from the lesson until now. This lesson improves my understanding of OOP. It is so easy, interesting and amazing to use python..."

"The instructor's explanations are incredibly clear; I felt I understood complex concepts quickly and efficiently."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete Course on Data Visualization, Matplotlib and Python with these activities:

Review Python and Pandas basics

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Review Python and Pandas basics to enhance prerequesite knowledge and solidify foundational concepts

Browse courses on Python

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Revisit Python syntax, data types, and control flow
Refresh Pandas data structures, like DataFrames and Series

Join a study group or discussion forum

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Join a study group or discussion forum to connect with peers, ask questions, and share knowledge

Browse courses on Matplotlib

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Identify relevant study groups or discussion forums
Actively participate in discussions and ask for help when needed

Compile notes from multiple sources

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This will build a stronger foundation to improve your readiness at the beginning of the course, when none of the material covered is new to you.

Browse courses on Data Visualization

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Gather all relevant materials
Review the material and identify key concepts
Organize the material into a logical structure
Create a study plan

12 other activities

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Explore Matplotlib's documentation

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Explore Matplotlib's documentation to familiarize yourself with its capabilities and best practices

Browse courses on Matplotlib

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Read the Matplotlib User Guide
Follow tutorials on creating specific chart types
Experiment with different Matplotlib features

Practice Matplotlib Functions

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Practicing Matplotlib functions will help you improve your understanding of how Matplotlib works, and will also help you refine your programming skills.

Browse courses on Matplotlib

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Find a set of Matplotlib functions to practice.
Write a Python script to practice using the functions.
Run the script and check the output.
Repeat steps 2-3 until you are comfortable using the functions.

Learn Matplotlib Axes

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Examining tutorials about Matplotlib Axes will help give you a better grounding in Matplotlib and how to use it.

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Search for tutorials on Matplotlib Axes
Follow the steps in the tutorials
Create your own custom charts using Matplotlib Axes

Discuss Matplotlib with Peers

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Discussing Matplotlib with peers will help you to learn from others, share your own knowledge, and get feedback on your work.

Browse courses on Matplotlib

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Find a peer group or online forum where you can discuss Matplotlib
Ask questions about Matplotlib
Share your own knowledge and experience with Matplotlib
Help others to troubleshoot their Matplotlib code

Create Python Matplotlib Visualization

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Creating a Matplotlib visualization will help you solidify your understanding of how Matplotlib works, and will also help you refine your programming skills.

Browse courses on Matplotlib

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Choose a dataset to visualize.
Import the Matplotlib library.
Create a figure and axes object.
Plot the data using the appropriate Matplotlib function.
Add a title, labels, and legend to the plot.

Create various chart types with Matplotlib

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Create various chart types with Matplotlib to reinforce practical skills and deepen understanding

Browse courses on Matplotlib

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Generate line charts, bar charts, and scatter plots
Customize chart elements, such as labels, colors, and legends
Create more complex charts, such as histograms and heatmaps

Practice Customizing Matplotlib Charts

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Practicing customizing Matplotlib charts will help you to develop your skills in using Matplotlib to create visually appealing and informative charts.

Browse courses on Matplotlib

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Find a dataset to visualize
Create a basic chart using Matplotlib
Customize the chart to change the appearance of the lines, markers, and axes
Add a title, labels, and a legend to the chart
Save the chart as an image or PDF

Write a Blog Post on Matplotlib

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Writing a blog post on Matplotlib will help you solidify your understanding of Matplotlib, and will also help you improve your writing and communication skills.

Browse courses on Matplotlib

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Choose a topic for your blog post.
Research the topic and gather information.
Write the blog post.
Edit and proofread the blog post.
Publish the blog post.

Create Data Visualizations with Matplotlib

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Creating your own data visualizations will give you hands-on experience with Matplotlib and help you to develop your understanding of how to use it to create effective visuals.

Browse courses on Matplotlib

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Choose a dataset to visualize
Select the appropriate chart type for your data
Create your chart using Matplotlib
Customize your chart to make it visually appealing
Save your chart as an image or PDF

Create a Data Visualization Project

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Creating a data visualization project will give you the opportunity to apply your skills in Matplotlib to a real-world problem.

Browse courses on Matplotlib

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Define the goal of your project
Choose a dataset to visualize
Select the appropriate chart type for your data
Create your chart using Matplotlib
Customize your chart to make it visually appealing
Write a report or give a presentation on your project

Develop a data visualization project

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Develop a data visualization project to apply and demonstrate your Matplotlib skills

Browse courses on Matplotlib

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Identify a dataset and define a visualization goal
Select appropriate Matplotlib charts and customize them
Create a presentation or report to showcase your project

Contribute to the Matplotlib Project

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Contributing to the Matplotlib project will give you the opportunity to learn more about Matplotlib and its development process, and to make a valuable contribution to the open source community.

Browse courses on Matplotlib

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Find a bug or feature request that you want to work on
Fork the Matplotlib repository
Make changes to the code
Submit a pull request
Respond to feedback from the Matplotlib team

Career center

Learners who complete Complete Course on Data Visualization, Matplotlib and Python will develop knowledge and skills that may be useful to these careers: