Learn Python Data Visualization: Matplotlib to Dash Course

Course Summary

Have you ever been confused by all the different python plotting libraries? Have you tried to make a "simple" plot and gotten stuck and been unable to move forward? Do you want to make sophisticated, interactive data visualizations in python? If you answer yes, to any of these questions, then this course is for you.

What students are saying

Effective PyCharm course is awesome. I have been using the IDE for a little while but you've opened up a whole world of features I never knew existed.
-- Nader S

Source code and course GitHub repository

github.com/talkpython/python-data-visualization

What's this course about and how is it different?

The python data visualization landscape has many different libraries. They are all powerful and useful but it can be confusing to determine what works best for you. This course is unique because you will learn about many of the most popular python visualization libraries. You will start by learning how to use each library to build simple visualizations. You will also explore more complex usage and identify the scenarios where each library shines.

By the end of this course, you will have a basic working knowledge of how to visualize data in python using multiple libraries. You will also learn which library is best for you and your coding style. Along the way, you'll learn general visualization concepts to make your plots more effective.

In addition to the overview material, we will cover some of the more complex, interactive visualization dashboard technologies.

What topics are covered

In this course, you will:

  • Review the python visualization landscape
  • Explore core visualization concepts
  • Use matplotlib to build and customize visualizations
  • Build and customize simple plots with pandas
  • Learn about seaborn and use it for statistical visualizations
  • Create visualizations using Altair
  • Generate interactive plots using the Plotly library
  • Design interactive dashboards using Streamlit
  • Construct highly custom and flexible dashboards using Plotly's Dash framework

View the full course outline.

Who is this course for?

Developers and Data Analysts that have some experience with python but have not developed a competency in a python visualization library. This course is also helpful for those that feel restricted by their current plotting tools and wish to explore other options.

Note: All software used during this course, including editors, Python language, etc., are 100% free and open source. You won't have to buy anything to take the course.

Concepts backed by concise visuals

While exploring a topic interactively with demos and live code is very engaging, it can mean losing the forest for the trees. That's why when we hit a new topic, we stop and discuss it with concise and clear visuals.

Here's an example of demonstrating the different available color palettes.

Example: Concepts backed by concise visuals

Who am I? Why should you take my course?

Who is Chris Moffitt? Hi, I'm Chris Moffitt. I am passionate about finding ways to use the power of Python to be more efficient and effective in a business setting. I've been using Python for over 15 years to solve a variety of real-world problems for everything from web development to system administration and most recently data science.

Follow along with subtitles and transcripts

Each course comes with subtitles and full transcripts. The transcripts are available as a separate searchable page for each lecture. They also are available in course-wide search results to help you find just the right lecture.

Each course has subtitles available in the video player.

This course is delivered in very high resolution

Example of 1440p high res video

This course is delivered in 1440p (4x the pixels as 720p). When you're watching the videos for this course, it will feel like you're sitting next to the instructor looking at their screen.

Every little detail, menu item, and icon is clear and crisp. Watch the introductory video at the top of this page to see an example.

Free office hours keep you from getting stuck

One of the challenges of self-paced online learning is getting stuck. It can be hard to get the help you need to get unstuck.

That's why at Talk Python Training, we offer live, online office hours. You drop in and join a group of fellow students to chat about your course progress and see solutions via screen sharing.

Just visit your account page to see the upcoming office hour schedule.

The time to act is now

Data sciense is one of the hottest topic of the year and data visualization is a core skillset needed to properly communicate your results and discoveries. Take this course to get good at a wide variety of modern Python-based visualization libraries.

Course Outline: Chapters and Lectures

Welcome to the course
8:21
Motivation
0:25
Statistics aren't enough
0:53
Why visualize data?
1:00
Why Python?
0:48
Python visualization ecosystem
0:37
Course objectives
0:54
Topic outline
1:21
Python check
1:06
Source code
0:23
Meet your instructor
0:54
Visualization Concepts
9:13
Intro to Visualization concepts
0:47
Aesthetics
1:22
Data types
0:52
Visualization variables
1:14
Colors
1:33
Small multiple plots
1:02
Analysis types
1:15
Working with data
1:08
Matplotlib
56:41
Matplotlib introduction
0:29
Matplotlib history
0:59
Matplotlib landscape
0:47
System setup
2:38
Data set
1:50
Figure overview
1:08
Interface types
1:41
Launching notebooks
1:13
Reading data
2:03
Pyplot example
2:13
Object Oriented API
4:47
Histograms
3:35
Figures and Axes
5:35
Saving images
1:52
Quick reference
1:14
Line plots
4:20
Bar charts
1:50
Scatter plots
5:26
Styles
2:51
Regression
3:15
Customizing multiple plots
3:35
References
1:40
Summary
1:40
Pandas
17:50
Pandas introduction
0:22
Pandas overview
0:52
API overview
1:33
Basic API examples
5:41
API summary
1:02
Specialized hist and boxplot API
1:00
Advanced specialized plots
5:02
Advanced plot summary
1:03
Pandas conclusion
1:15
Seaborn
39:31
Introduction to Seaborn
0:30
Seaborn overview
1:41
Getting started
0:58
Figure and axes level plots
1:58
Data set changes
1:54
Displot
4:17
Catplot
3:33
Relplot
1:47
Seaborn API summary
1:23
Displot relplot and facetting
4:40
Catplot API summary
3:55
Specialized plots
1:09
Heatmap
4:33
Pair and jointplot
4:31
Customizing Seaborn summary
1:26
Seaborn summary
1:16
Altair
38:00
Introduction to Altair
0:42
Overview
1:02
Vega lite
1:16
Installing
0:57
Shorthand API
1:27
Basic shorthand API
3:48
Additional examples of the basic API
2:56
Longhand API
3:39
Longhand overview
1:37
Data type
1:26
Types viz alterations
1:24
Concat charts
2:34
Faceting
1:22
Layers
2:13
Multiple chart summary
0:58
Amazon data set
2:52
Amazon authors
5:20
Reference example
1:09
Conclusion
1:18
Plotly
33:39
Introduction to Plotly
0:34
Overview
1:06
API intro
1:08
Installing
0:54
Basic plotting
3:03
Customizing
2:43
Additional plot types
3:43
API overview
1:33
Scatter plots
3:17
Line bar area
2:38
Regression treemap heatmap
4:54
Facetting
3:22
Annotations
2:42
Annotation summary
0:51
Conclusion
1:11
Streamlit
25:27
Introduction
0:32
Background
0:57
Installation
0:56
Basic app concepts
0:59
Simple app example
2:32
Streamlit running overview
2:06
API summary
1:32
Widget Intro
2:43
Widget interactivity
1:13
User input
2:33
Show charts
3:00
Sidebar intro
2:44
Sidebar details
2:30
Conclusion
1:10
Dash
33:49
Intro
0:35
Overview
0:46
Why Dash?
0:55
Getting started
0:35
Program structure
1:02
First app
2:49
Running app
2:20
Component overview
1:40
HTML
3:42
Interactive app
3:41
Interactive app demo
1:48
Callback reference
0:42
Final app overview
0:41
Full app part 1
3:33
Full app data filtering
4:27
Full app demo
2:13
Advanced topics
0:57
Conclusion
1:23
Course Conclusion
12:27
Course review
1:14
Objectives
1:13
Data vis concepts
1:04
Matplotlib
1:24
Pandas
0:59
Seaborn
1:12
Altair
1:07
Plotly
0:48
Streamlit
0:49
Dash
0:57
My workflow
1:06
Thank you
0:34
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