Garfield Stinvil
Garfield is an experienced software trainer with over 16 years of real-world professional experience. He started as a data analyst with a Wall Street real estate investment company & continued working in the professional development department at New York Road Runners Organization before working at Noble. He enjoys bringing humor to whatever he teaches and loves conveying ideas in novel ways that help others learn more efficiently.
Since starting his professional training career in 2016, he has worked with several corporate clients including Adobe, HBO, Amazon, Yelp, Mitsubishi, WeWork, Michael Kors, Christian Dior, and Hermès.
Outside of work, his hobbies include rescuing & archiving at-risk artistic online media using his database management skills.
Learning Resources by Garfield Stinvil
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Visualizing Data: Crafting Butterfly Charts for Comparison
Discover how to create striking butterfly charts to compare opposing data like sales vs. quantity or gender demographics. Learn the step-by-step process for building simple to advanced versions using tools like Tableau.
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Life Expectancy Visualizations in Excel and Tableau
Learn how to transform messy life expectancy data into a clean, visual-ready format using Excel's Power Query and Tableau. Discover tips for unpivoting data, building pivot tables, and highlighting global insights.
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Mastering Bar and Column Charts
Learn how to create and customize bar and column charts using simple drag-and-drop techniques. This guide walks you through formatting, labeling, and styling your visualizations for polished, professional results.
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Creating a Custom State Selector
Learn how to build a dynamic State Selector in Tableau using parameters and calculated fields for interactive filtering. This guide walks you through creating a customizable control to view sales data by individual state or all states at once.
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Optimizing Your Data Workflow in Tableau
Learn how to properly prepare and connect your data in Tableau Public for seamless visualization. Discover essential tips on data structuring, cleaning, and using the popular Superstore Sales dataset.
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