
Moving beyond basic scatter plots and bar charts, this course teaches how to craft effective visual reports in Python. By analyzing US air pollution and farmers market datasets, you learn how audience considerations, color selection, and uncertainty mapping influence data communication.
Intermediate Python users and data analysts seeking to upgrade basic graphics into impactful, audience-ready data visualizations.
Great data visualization is the cornerstone of impactful data science. Visualization helps you to both find insight in your data and share those insights with your audience. Everyone learns how to make a basic scatter plot or bar chart on their journey to becoming a data scientist, but the true potential of data visualization is realized when you take a step back and think about what, why, and how you are visualizing your data. In this course you will learn how to construct compelling and attractive visualizations that help you communicate the results of your analyses efficiently and effectively. We will cover comparing data, the ins and outs of color, showing uncertainty, and how to build the right visualization for your given audience through the investigation of a datasets on air pollution around the US and farmer's markets. We will finish the course by examining open-access farmers market data to build a polished and impactful visual report.
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This course is free to enrol.

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