Série: A ciência da Visualização de Dados - Cap I

Version 3

    Caros boa noite.

    Um grande prazer passar aqui para compartilhar com vocês alguns artigos que envolvem a Ciência da Visualização de Dados.

    Sugiram, acompanhem e compartilhem as informações pois postarei periodicamente materiais dos principais gurus do tema e fiquem a vontade em contribuir.


    Vejam abaixo como nosso cérebro é capaz de nos ajudar a dar sentido aos dados (as vezes de forma mais lenta) e com um pouco de fundamentação teórica, apresentar visões mais adequadas.


    Data Visualization for Human Perception (by Stephen Few)

    Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication. Important stories live in our data and data visualization is a powerful means to discover and understand these stories, and then to present them to others. The information is abstract in that it describes things that are not physical. Statistical information is abstract. Whether it concerns sales, incidences of disease, athletic performance, or anything else, even though it doesn't pertain to the physical world, we can still display it visually, but to do this we must find a way to give form to that which has none. This translation of the abstract into physical attributes of vision (length, position, size, shape, and color, to name a few) can only succeed if we understand a bit about visual perception and cognition. In other words, to visualize data effectively, we must follow design principles that are derived from an understanding of human perception.

    As the saying goes, "a picture is worth a thousand words" - often more - but only when the story is best told graphically rather than verbally and the picture is well designed. You could stare at a table of numbers all day and never see what would be immediately obvious when looking at a good picture of those same numbers. Allow me to illustrate. Here's a simple table of sales data - a year's worth - divided into two regions:


    This table does two things extremely well: it expresses these sales values precisely and it provides an efficient means to look up values for a particular region and month. But if we're looking for patterns, trends, or exceptions among these values, if we want a quick sense of the story contained in these numbers, or we need to compare whole sets of numbers rather than just two at a time, this table fails.

    Now look at the following picture of the same information in the form of a line graph:



    Several facts now leap into view:

    • Domestic sales were considerably and consistently higher than international.
    • Domestic sales trended upward over the year as a whole.
    • International sales, in contrast, remained relatively flat, with one glaring exception: they decreased sharply in August.
    • Domestic sales exhibited a cyclical pattern - up, up, down - that repeated itself on a quarterly basis, always reaching the peak in the last month of the quarter and then declining dramatically in the first month of the next.

    What these numbers could not communicate when presented as text in a table, which our brains interpret through the use of verbal processing, becomes visible and understandable when communicated visually.


    This is the power of "data visualization."

    Um grande abraço a todos e até o próximo artigo;


             Danilo Vasconcelos



    Nossa Missão: “Ajudar pessoas a tomar as melhores decisões baseadas em análise de dados de forma interativa e colaborativa”

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