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 knowledge snap shots or visualization is quick changing into a circulation of its personal. In information issues: Visualization that suggests whatever, writer Nathan Yau offers an interesting supplement to his bestseller Visualize This, this time targeting the pics aspect of knowledge research. utilizing examples from artwork, layout, enterprise, statistics, cartography, and on-line media, he explores either standard-and now not so standard-concepts and ideas approximately illustrating data.
stocks fascinating rules from Nathan Yau, writer of Visualize This and author of flowingdata.com, with over 66,000 subscribers
specializes in visualization, info portraits that aid audience see tendencies and styles they may not in a different way see in a table
contains examples from the author's personal illustrations, in addition to from execs in facts, paintings, layout, company, laptop technological know-how, cartography, and more
Examines general principles throughout all visualization functions, then explores whilst and the place you could holiday these rules
Create visualizations that sign up in any respect degrees, with info issues: Visualization that implies anything.
Read or Download Data Points: Visualization That Means Something PDF
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Extra resources for Data Points: Visualization That Means Something
That’s going to cause problems because then the index works only under the assumption that the literacy rate one decade earlier is comparable to the present, which might not be (and probably isn’t) the case. What: Ultimately, you want to know what your data is about, but before you can do that, you should know what surrounds the numbers. Talk to subject experts, read papers, and study accompanying documentation. 38 | CHAPTER 1: Understanding Data In introduction statistics courses, you typically learn about analysis methods, such as hypothesis testing, regression, and modeling, in a vacuum, because the goal is to learn the math and concepts.
Visualization is a medium: a way to explore, present, and express meaning in data. Rather than disjoint categories that work independently from others, you can think of visualization as a continuous spectrum that stretches from statistical graphics to data art. There is visualization that is clearly one or the other, but there are many works that are a blend of both and can’t be put in a bin. Where statistics, design, and aesthetics find a balance is where a lot of the best work comes from. This is not to say that the blend is always best, nor are statistical graphics better than data art, or vice versa.
What: Ultimately, you want to know what your data is about, but before you can do that, you should know what surrounds the numbers. Talk to subject experts, read papers, and study accompanying documentation. 38 | CHAPTER 1: Understanding Data In introduction statistics courses, you typically learn about analysis methods, such as hypothesis testing, regression, and modeling, in a vacuum, because the goal is to learn the math and concepts. But when you get to real-world data, the goal shifts to information gathering.