By Nathan Yau
No matter if it's statistical charts, geographic maps, or the snappy graphical records you notice in your favourite information websites, the paintings of information photos or visualization is quick changing into a circulate of its personal. In facts issues: Visualization that suggests anything, writer Nathan Yau offers an exciting supplement to his bestseller Visualize This, this time targeting the pix part of information research. utilizing examples from artwork, layout, enterprise, facts, cartography, and on-line media, he explores either standard-and no longer so standard-concepts and concepts approximately illustrating data.
stocks exciting principles from Nathan Yau, writer of Visualize This and writer of flowingdata.com, with over 66,000 subscribers
specializes in visualization, info pictures that support audience see developments and styles they won't differently see in a table
comprises examples from the author's personal illustrations, in addition to from pros in facts, paintings, layout, company, desktop technological know-how, cartography, and more
Examines average principles throughout all visualization functions, then explores whilst and the place you could holiday these rules
Create visualizations that sign in in any respect degrees, with information issues: Visualization that implies whatever.
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Additional resources for Data Points: Visualization That Means Something
Who: A quote in a major newspaper carries more weight than one from a celebrity gossip site that has a reputation for stretching the truth. Similarly, data from a reputable source typically implies better accuracy than a random online poll. For example, Gallup, which has measured public opinion since the 1930s, is more reliable than say, someone (for example, me) experimenting with a small, one-off Twitter sample late at night during a short period of time. Whereas the former works to create samples representative of a region, there are unknowns with the latter.
When: Most data is linked to time in some way in that it might be a time series, or it’s a snapshot from a specific period. In both cases, you have to know when the data was collected. An estimate made decades ago does not equate to one in the present. This seems obvious, but it’s a common mistake to take old data and pass it off as new because it’s what’s available. Things change, people change, and places change, and so naturally, data changes. Where: Things can change across cities, states, and countries just as they do over time.
Forget this stupid job. ” And my 1-month obsession with blackjack began. ) In case you’re unfamiliar with the game, here’s a quick rundown. There’s a dealer and a player. The dealer deals two cards to each (one of his is face down), and the goal is to get a card total as close to 21 as possible, without going over. You can choose to take additional cards (called a hit) or not (stay). In some cases, you can also split your hand of two cards, as if you’re playing two separate hands; you can also double down, which means to double your bet.
Data Points: Visualization That Means Something by Nathan Yau
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