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Often confused with data visualization, data presentation architecture is a much broader skill set that includes determining what data on what schedule and in what exact format is to be presented, not just the best way to present data that has already been chosen. Data visualization skills are one element of DPA."
Data visualization uses information displays (graphics such as, tables and charts) to help communicate key messages contained in the data. [46] Tables are a valuable tool by enabling the ability of a user to query and focus on specific numbers; while charts (e.g., bar charts or line charts), may help explain the quantitative messages contained ...
Information visualization handles abstract data structures such as trees or graphs. Visual analytics is especially concerned with coupling interactive visual representations with underlying analytical processes (e.g., statistical procedures, data mining techniques) such that high-level, complex activities can be effectively performed (e.g ...
Hands-On Data Visualization details design methods for interactive charts and customized maps, starting with drag-and-drop tools including Google Sheets, Datawrapper, and Tableau Public. It progresses to detail methods for editing source code templates built with Chart.js, Highcharts, and Leaflet on GitHub.
Infogram is a web-based data visualization and infographics platform, created in Riga, Latvia. [1] It allows people to make and share digital charts, infographics and maps. Infogram offers an intuitive WYSIWYG editor that converts users’ data into infographics that can be published, embedded or shared. Users do not need coding skills to use ...
Therefore, it is crucial to identify the appropriate visualization for the data set and infographic by taking into consideration graphical features such as position, size, shape, and color. There are primarily five types of visualization categories – time-series data, statistical distributions, maps, hierarchies, and networking. [3]
The field encompasses preparing data for analysis, formulating data science problems, analyzing data, and summarizing these findings. As such, it incorporates skills from computer science, mathematics, data visualization, graphic design, communication, and business. [11] Vasant Dhar writes that statistics emphasizes quantitative data and ...
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.
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