Complete Course on Data Visualization, Matplotlib and Python

Master Matplotlib Anatomy and Learn Seaborn to Visualize Data with Custom, Beautiful Charts, Suitable for All Purposes

What you'll learn:

• 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

COURSE IN THE NUTSHELL

1. Concise and to the point, asI appreciate your time and don't have the luxury to tell you my story

2. Easy to understand and tailored for a broad audience, as it only requires a basic knowledge of Python and only

WHAT STUDENTS SAY

"This is a great course! 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

TELL ME MORE...

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

1. How Matplotlib Works

2. How to create charts from simple to scientific ones with Matplotlib, Pandas and Seaborn

3. How to customizechartsofany 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 andhow 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 ofcharts using MatplotlibOOP, andPandas and mix them together toachieve the maximum efficiency and granularcontrol over graphs.

Axes StatisticalCharts

Here we will learn how to make statistical charts such as Auto Correlation, Boxplots, Violinplots and KDE plots withMatplotlibOOP 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 andsuper easy to learn. And in this section, we will create Regression plots, Count plots, Barplots, Factorplots, Jointplots,Boxplots, Violin plots and more.

Course Summary andExercises

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 othersections if you seek more granular coverage of the topic or when you have more time available.

TOOLS USED

1. Jupyter Notebook (IDE)

2. Matplotlib 2.x

3. Seaborn 0.8.1 or above

4. Pandas 0.22 or above