What is NOT Data Visualization?

 

Reaching to data is important… What is equally important is to make it simple and to present to the ones that are interested. There are many ways to evaluate this. Data visualization is one of the methods that is used to make data readable. In summary, data visualization means the creation of a visual representation of the data. So, what is not data visualization?

Data Visualization is NOT Complex.

Although it might appear to be difficult at the beginning,but data visualization is not complicated. Its main purpose is to provide an easy understanding of the data. It makes complex data more accessible, understandable and available. In doing so, it uses different tables and graphs. The data group desired by the user can thus be easily captured from the overall big data data.

Data Visualization is Both an Art and a Science.

News and discoveries have now been replaced by stories… Data visualization is also a method of transforming big data into stories. It is seen by some as a branch of descriptive statistics. With this aspect it can be perceived as an art. On the other hand, it makes complex and intricate data much more understandable. Data generated by the Internet activity, and by the Internet of things gathered and collected and re-expressed through communication. Processing, analyzing and communicating these data presents ethical and analytical challenges for data visualization. Data visualization aims to find a solution to these possible problems.

Data Visualization Is NotNew.

Your search for data visualization brought you to the center of the latest technology. However, the beginning of this discipline dates way back! Data visualization has no comprehensive history… yet! This discipline, which encompasses the development of visual thought and the visual representation of data and blends the contributions of different disciplines, is old as the history of humanity. The bristle figurines reflecting daily life in Mesopotamia, the history of the knots that Incas made to record events, the papyrus recordings of the Egyptians… They are all early examples of data visualization!

Data Visualization Is Not A Single Discipline.

Data visualization is an area where many different disciplines come together. Data engineering is one of these areas. Data engineers are preparing the data group to be visualized. Visual communicators make this knowledgeable group visually perceptible. Graphic designers interpret these images in different ways. Business analysts, data architects, and visual journalists also benefit from this data. In short, data visualization affects countless areas.

Data Visualization Is Not Hard.

Today, many different computer programs are used for data visualization. Most of us are familiar with the Excel from Microsoft Office (or Pages as it’s Mac equivalent) is a known example. To visualize larger and complex data more easily, many different programs are being used. In the field of statistics, SAS, SOFA, R, Minitab and Cornerstone are preferred. Apart from these, programs such as D3, Python and JavaScript can be selected to examine different quantitative data.

Data Visualization Is Not Boring.

On the contrary, it is one of the most fun practices about data engineering! Verification is separate to open it. Let’s look at them for the most frequently produced verification!

Bar Chart: Usually it compares the data in terms of value. Performance evaluation can be given as an example.

Histogram: Evaluates the data within a certain period of time. Annual sales figures can be visualized by histogram.

Scatter plot: A scatter plot can be used to see the correlation between two different variables. In order to look at the correlation between unemployment and inflation values in a given period, it is possible to choose a scatter plot. These graphics can be done in 3D as well.

Network Analysis: It is the ideal method to see the relationship between different data groups, to identify bridges and to determine groupings.

Heat Map: This method where colors are used skillfully is preferred especially in risk analysis



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