Ask whether a visualization is necessary and effective
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Visualizations can fundamentally surprise us
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Visualizations are mappings, encoded and decoded
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Types of data and attributes
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Some quantitative encodings are easier to decode
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Common versus noncommon scales
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Faceting: four variables with position
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Preattentive attributes are easily perceived...
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Example: bar chart
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References and further reading (all on Amazon)
Description:
Learn how to create effective data visualizations using Python libraries Matplotlib and Bokeh in this hands-on training video from ODSC West 2019. Explore different types of visualizations, techniques for reducing clutter, and methods for guiding the viewer's eye. Discover when and how to add interactivity with Bokeh, and gain insights into visual design principles that enhance communication of data insights. Suitable for beginners and experienced data scientists alike, this 36-minute talk covers the importance of visualization in the data science workflow, common encoding techniques, and best practices for creating clear and compelling visuals to effectively convey information to various audiences.
Data Visualization - From Square One to Interactivity