What is the role of faceting in data visualization?

What is the role of faceting in data visualization?

How to know what distribution my data has

Not many days ago we highlighted, in this blog, the important advances experienced in the field of scientific research, mainly due to the evolution of data analysis and visualization tools. And also, in previous posts, we devoted special attention to review other uses and applications of Big Data away from its original natural scope (finance and insurance, mainly), making visible possible uses that today are already providing, among other possibilities, the opportunity to have a more personalized medical care or educational offer, among others.

When we speak of wellbeing, a wide range of mechanisms and gears come to mind that come into play in the acquisition of a certain level of wellbeing and are related, in turn, to many other aspects of a personal and social nature, all of them referring, more or less directly, to the fundamental issue in which the consideration of any type of wellbeing is rooted: happiness.

Data distribution in r

Data visualization is responsible for giving the appropriate hierarchy and relevance that each piece of data contains. The representation of information in digital and interactive format is a characteristic of data visualization that in the case of the pandemic has to be given almost in real time, and the use of digital tools that process information from databases has encouraged the use of this type of resource because it is practical, fast and easy to use.

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It is known that this was not the first map concerning a health issue, An Inquiry into the Cause of the Prevalence of the Yellow Fever in New York, is considered the first of its kind, although it is also known that in the early 19th century in Europe several physicians used maps to understand the environmental aspects of epidemics[6].

The visualization consultant Amanda Makulec suggests that care should be taken in how coronavirus cases are represented, since the data are not accurate; she describes in a series of infographics[7] how information is obtained on a patient who tests positive for this virus in Washington DC, from exposure to obtaining positive or negative test results, in a period of approximately eleven days, so that the data that are socialized every day may change.

Theory of the two facets of the state

The title of this post, Statistics, women and health, is intended to be, with every intention, provocative. To provoke the reader to wonder what statistics and health have to do with each other. And what do statistics and health have to do with the gender perspective. And this provocation refers, immediately, to the debates about the different classifications of the areas or branches of knowledge.

It is not the aim of this article to address these debates, but rather I intend to go in the opposite direction. To focus, with absolute modesty, on defending the need to construct frameworks of analysis that overcome this compartmentalization of knowledge and science into areas. I believe that only from global visions will it be possible to successfully address the phenomenal challenges facing our society.

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Therefore, her role in the field of health is key because she was a great promoter of health reform worldwide, at a time when health conditions were extremely difficult. And she dedicated part of her life to promoting the development of nursing as a modern profession.

Facets meaning

Exaggerated titles aside, what is certain is that discipine offers an increasingly mature set of knowledge oriented to exploit data to extract knowledge. The techniques and principles that the data science community has developed can be exploited in many fields. Among them, the social sciences, which are also undergoing a transformation and incorporating analytical programming as an increasingly widespread resource.

Advancing the frontiers of data science, creating the algorithms and computational techniques that open up new possibilities for analysis is a complex task, carried out by specialists with deep knowledge of mathematics. And yet “using” data science, applying its principles to solve complex problems, is much easier.

We have already said that data science is about using programming techniques to analyze data. But it’s not just that; applied data science requires the development of skills in four areas:

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