Start with Edward Tufte’s work on information design. His books—especially “The Visual Display of Quantitative Information”—laid the groundwork for clear, data-driven visuals. Tufte argues for maximizing data density while minimizing “chartjunk.” You’ll see examples of simple line charts stripped to essentials, small multiples that let you compare trends at a glance, and sparklines embedded in text for instant context. His methods forced statisticians and designers to rethink how they present numbers, cutting out unnecessary borders, grid lines, and decorative elements. Next up: Jacques Bertin’s “Semiology of Graphics.” Bertin breaks down data visualization into visual variables—position, size, shape, value, color, orientation, and texture—and shows you how to combine them for precise, layered views of complex data. He provides a systematic framework: map each data dimension to the right visual variable, test for legibility, then iterate. Companies and governments use Bertin’s approach to build dashboards that track dozens of metrics without confusing users. The tweet even claims “billions of dollars have been reallocated based on the lessons of this text,” pointing to large-scale budget shifts driven by clearer presentations of spending vs. outcomes. Together, these two texts form a curriculum. Start with Tufte to learn minimalist, high-data-density graphics. Then move to Bertin for a toolkit that handles multi-variable datasets. Mastering both lets you turn rows and columns into visual stories that drive decisions—from boardroom debates to public policy reallocations.