Python Data Visualization Course
Source code and course GitHub repositorygithub.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.
Who am I? Why should you take my course?
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.
This course is delivered in very high resolution
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.
Questions? Send us an email: email@example.com