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Indigo AI for Data Visualizers: Choose clearer charts and explain findings to decision-makers | Erin Mills Town Centre
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You have data, but turning it into a chart that people understand-and act on-can be surprisingly difficult. AI for Data Visualizers: From Dataset to Story shows you how to move from messy datasets and vague questions to clear visual evidence and persuasive decision stories.Maya Ellison presents a practical guide to data visualization with AI, including ChatGPT-supported workflows for exploring data, selecting charts, testing interpretations, and verifying results. You'll learn how to start with the decision rather than the graphic, prepare unreliable data for analysis, match chart types to real questions, and use AI without surrendering your judgment.The book's focused chapters take you through the complete process: framing the problem, turning raw data into usable evidence, prompting and checking AI analysis, shaping findings for decision-makers, and building a repeatable visualization workflow. An introduction establishes the principles, while the conclusion helps you bring them together in everyday practice.Whether you create dashboards, reports, presentations, or exploratory analyses, this book will help you choose clearer charts, communicate uncertainty responsibly, and make your data stories more useful.
You have data, but turning it into a chart that people understand-and act on-can be surprisingly difficult. AI for Data Visualizers: From Dataset to Story shows you how to move from messy datasets and vague questions to clear visual evidence and persuasive decision stories.Maya Ellison presents a practical guide to data visualization with AI, including ChatGPT-supported workflows for exploring data, selecting charts, testing interpretations, and verifying results. You'll learn how to start with the decision rather than the graphic, prepare unreliable data for analysis, match chart types to real questions, and use AI without surrendering your judgment.The book's focused chapters take you through the complete process: framing the problem, turning raw data into usable evidence, prompting and checking AI analysis, shaping findings for decision-makers, and building a repeatable visualization workflow. An introduction establishes the principles, while the conclusion helps you bring them together in everyday practice.Whether you create dashboards, reports, presentations, or exploratory analyses, this book will help you choose clearer charts, communicate uncertainty responsibly, and make your data stories more useful.
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