Course
Improving Your Data Visualizations in Python
IntermediateSkill Level
Updated 01/2026PythonData Visualization4 hr15 videos54 Exercises4,650 XP19,003Statement of Accomplishment
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Prerequisites
Python ToolboxIntroduction to Data Visualization with MatplotlibIntroduction to Data Visualization with Seaborn1
Highlighting Your Data
How do you show all of your data while making sure that viewers don't miss an important point or points? Here we discuss how to guide your viewer through the data with color-based highlights and text. We also introduce a dataset on common pollutant values across the United States.
2
Using Color in Your Visualizations
Color is a powerful tool for encoded values in data visualization. However, with this power comes danger. In this chapter, we talk about how to choose an appropriate color palette for your visualization based upon the type of data it is showing.
3
Showing Uncertainty
Uncertainty occurs everywhere in data science, but it's frequently left out of visualizations where it should be included. Here, we review what a confidence interval is and how to visualize them for both single estimates and continuous functions. Additionally, we discuss the bootstrap resampling technique for assessing uncertainty and how to visualize it properly.
4
Visualization in the Data Science Workflow
Often visualization is taught in isolation, with best practices only discussed in a general way. In reality, you will need to bend the rules for different scenarios. From messy exploratory visualizations to polishing the font sizes of your final product; in this chapter, we dive into how to optimize your visualizations at each step of a data science workflow.
Improving Your Data Visualizations in Python
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FAQs
What makes this course different from introductory visualization courses?
This course goes beyond basic charts to teach you how to think critically about what, why, and how you visualize data, focusing on clarity, color use, uncertainty, and audience.
What datasets are explored in this course?
You will investigate datasets on air pollution across the US and open-access farmers market data to build polished, impactful visual reports.
Does this course cover how to show uncertainty in visualizations?
Yes. One of the core topics is showing uncertainty in your visualizations, which is critical for honest and accurate data communication.
Which Python visualization libraries are used?
The course builds on Matplotlib and Seaborn, which you should already know from the prerequisite courses, to create more refined and effective visualizations.
What will the final project involve?
You will use open-access farmers market data to build a polished and impactful visual report, applying all the techniques for color, comparison, uncertainty, and audience-aware design.
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