Difference between revisions of "Visual Analytics"
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| − | == What is meant by | + | == What is meant by Visual Analytics? == |
| − | + | '''Visual Analytics''' is a combination of data analysis, statistical analysis, and visual representation of data that helps users to gain insights and understanding from data. It involves using interactive visual displays of data and information to support the analysis and exploration of data, and to communicate the results of that analysis to others. | |
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| + | The goal of visual analytics is to provide users with the ability to understand, analyze, and communicate complex data in a way that is both intuitive and efficient. By using visual representations of data, such as charts, graphs, maps, and other types of visualizations, users can quickly and easily identify patterns, trends, and relationships in the data that may not be immediately apparent from looking at raw data alone. | ||
| + | Visual analytics tools and techniques can be used in a variety of contexts, including business intelligence, data science, and scientific research. They are especially useful for exploring and understanding large or complex datasets, or for identifying trends and patterns in real-time data streams. | ||
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*[[Advanced Analytics]] | *[[Advanced Analytics]] | ||
*[[Big Data]] | *[[Big Data]] | ||
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==References== | ==References== | ||
<references /> | <references /> | ||
Latest revision as of 15:21, 3 January 2023
What is meant by Visual Analytics?
Visual Analytics is a combination of data analysis, statistical analysis, and visual representation of data that helps users to gain insights and understanding from data. It involves using interactive visual displays of data and information to support the analysis and exploration of data, and to communicate the results of that analysis to others.
The goal of visual analytics is to provide users with the ability to understand, analyze, and communicate complex data in a way that is both intuitive and efficient. By using visual representations of data, such as charts, graphs, maps, and other types of visualizations, users can quickly and easily identify patterns, trends, and relationships in the data that may not be immediately apparent from looking at raw data alone.
Visual analytics tools and techniques can be used in a variety of contexts, including business intelligence, data science, and scientific research. They are especially useful for exploring and understanding large or complex datasets, or for identifying trends and patterns in real-time data streams.
See Also