What Is Data Visualization?

The concept of data visualization is Facebook's global user heat map, which is popular on the Internet. This map first extracts user information from Hive, then uses statistical software R for data mining, then draws lines based on defined weight values, and finally uses a color wheel to identify obtain.

Data visualization concept

Right!

Data Visualization and Information Visualization are two similar terms in the professional field.

• Digital visualization in the narrow sense refers to the presentation of data in the form of statistical charts, while information graphics (information visualization) refers to the visualization of non-numeric information. The former is used to convey information, and the latter is used to represent abstract or complex concepts, technologies and information.

• Data visualization is a type of information visualization in a broad sense, because information includes: digital and non-digital.

• From the interpretation of the original words: the emphasis of data visualization is "visualization", and the emphasis of information visualization is "graphicalization".

• Overall: Visualization is a general term for graphic technology in data, information, science and other fields.

Data visualization originated from computer graphics in the 1960s. People use computers to create graphical charts, visualize the extracted data, and present various attributes and variables of the data. With the development of computer hardware, people have created more complex and larger digital models, and developed data acquisition equipment and data preservation equipment. Similarly, more advanced computer graphics techniques and methods are needed to create these large-scale data sets. With the expansion of the data visualization platform, the increase of application fields, the continuous change of expressions, and the addition of real-time dynamic effects, user interaction, etc., the boundaries of data visualization continue to expand like all emerging concepts.

The pie charts, histograms, scatter charts, and histograms that we are familiar with are the most primitive statistical charts. They are the most basic and common application of data visualization. As a statistical tool, you can create a quick way to get to know your dataset and become a convincing means of communication. Communicate the basic information that exists in the data. So we can see statistical graphics in a large number of PPTs, reports, programs and news.

But the most primitive statistical charts can only present basic information, discover the structure in the data, and visualize quantitative data results. Faced with complex or large-scale heterogeneous data sets, such as business analysis, financial statements, population status distribution, media effect feedback, user behavior data, etc., the situation of data visualization will be much more complicated.

Generally, a series of complex data processing including data collection, analysis, governance, management, and mining are then designed by the designer to be a three-dimensional, two-dimensional, dynamic, real-time, or interactive. Engineers then create corresponding visualization algorithms and technical implementations. Including modeling methods, architectures for processing large-scale data, interactive technologies, and scaling methods. Animation engineers consider surface textures, animation rendering methods, and so on. Interaction designers also get involved in designing user interaction behavior patterns.

The creation of large-scale data visualization works or projects requires the collaboration of professionals in multiple fields to succeed, especially BI business intelligence. Human beings being able to manipulate and interpret such diverse and intricate cross-domain information is an art in itself.

After explaining the above content, the reader will understand why EXCEL is used to complete the data visualization.

The development of data visualization is the same as that of most projects. It is also based on the requirements to filter based on the data dimensions or attributes, and select the performance mode according to the purpose and user group. The same piece of data can be visualized into a variety of seemingly different forms.

• Some visualization goals are for observing and tracking data, so we need to emphasize real-time, change, and computing power, and may generate a constantly changing and readable chart.

• Some are for data analysis, so the data's presentation degree should be emphasized, and a retrievable and interactive chart may be generated.

• Some may generate distributed multi-dimensional charts in order to discover potential associations between data.

• Some of them use beautiful colors and animations to create vivid, clear, and attractive charts in order to help ordinary users or business users quickly understand the meaning or changes of data.

• Others are used for education, propaganda, or politics, made into posters, courseware, and appear on the streets, advertising handhelds, magazines, and rallies. This type of visualization has strong persuasive power. Using strong contrast, permutation and other means, it can create highly impactful self-referencing images. Many media abroad will hire designers to create visual charts to assist with news topics based on news topics or data.

The application value of data visualization, its diversity and expressiveness have attracted many practitioners, and each link in its creative process has strong professional background support. Whether it is dynamic or static visual graphics, it has built a new bridge for us, allowing us to gain insight into the world, discover all kinds of relationships, feel the changes in the information surrounding us, and allow us to understand other Things that are not easy to discover in the form.

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