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What are the uses of data visualization?
Data Visualization defines the visual representation of data with the support of comprehensive charts, images, lists, charts, and multiple visual objects. It allows users to simply learn the data within a fraction of time and extract useful data, patterns, and trends. Furthermore, it creates the data simply to understand.
In other terms, it can say that data representation in graphical form so that users can simply comprehend the process of trends in the data is known as data visualization.
There are several tools contained in data visualization, including chart maps, graphs, etc. The tools used for data visualization support the users in simply understanding and collecting the information supported by visual representation instead of going through the entire scanning of the datasheets.
Data visualization defines the data in visual form. It is essential because it allows data to be more easily seen. Machine learning technique plays an essential act in conducting predictive analysis which supports data visualization.
Data visualization is not only useful for business analysts, data analysts, and data scientists, but it plays an essential role in comprehending data visualization in any career. Whether it can work in design, operation, tech, marketing, sales, or multiple fields, it is required to visualize data.
Visualization provides data cleaning by discovering incorrect values (e.g., patients whose age is 999 or −1), missing values, duplicate rows, columns with all the equal values, and the like.
Visualization techniques are also beneficial for variable derivation and selection − they can support determining which variables to involve in the analysis and which can be redundant. They can also support with deciding suitable bin sizes, must binning of numerical variables be required. They can also play an act in combining elements as part of the data reduction phase.
Finally, if the data have yet to be collected and collection is expensive, visualization methods can help determine, using a sample, which variables and metrics are useful.
Data exploration is a compulsory original step whether or not more formal analysis follows. Graphical exploration can provide free-form exploration for the goals of learning the data structure, cleaning the information, recognizing outliers, discovering original patterns, and making interesting questions.
Graphical exploration can also be more targeted, geared toward definite questions of interest. In the data mining context, a combination is required − free-form exploration implemented to provide a specific goal.
Graphical exploration can range from making very basic plots to using operations including filtering and zooming interactively to analyze a group of interconnected visualizations that involve advanced features including color and multiple panels.
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