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What Is Data Visualization? How It Is Helpful?

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By Author: Tarun Teja
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Data Visualization

"Think, Stuart Card, Jock Mackinlay, and Ben Shneiderman collected the best academic work that had been done by that time into a single volume and made its discoveries accessible beyond the walls of academia ( Card et al 1999). Since the turn of the 21st century, data visualization has been popularized, too often in tragically ineffective ways as it has reached the masses through commercial software products. This display, consisting of multiple views of the same data set, was created using Tableau Software, one of the few software vendors that currently understand data visualization. Among those who have contributed to our understanding of data visualization, Colin Ware has done the most to base its practice on an understanding of human perception.

Data visualization tools provide data visualization designers with an easier way to create visual representations of large data sets. The best data visualization tools on the market have a few things in common. Some have excellent documentation and tutorials and are designed in ways that feel intuitive to the user. In fact, the very best can even handle multiple sets of data in a single visualization.
Data visualizations are meant to present data in a way that makes the information easy to digest and understand at a glance. The best practices below can be used to create your data visualizations in a way that is useful and clear to viewers. Data visualization can be viewed as a visual form of analysis with the purpose of making critical items jump out at the user. Data visualization is the act of taking information ( data ) and placing it into a visual context, such as a map or graph. Data visualizations make big and small data easier for the human brain to understand, and visualization also makes it easier to detect patterns, trends, and outliers in groups of data. Good data visualizations should place meaning into complicated datasets so that their message is clear and concise. Data visualization is the presentation of quantitative information in a graphical form. In other words, data visualizations turn large and small datasets into visuals that are easier for the human brain to understand and process. A combination of multiple visualizations and bits of information are often referred to as infographics. Data visualization helps to tell stories by curating data into a form easier to understand, highlighting the trends and outliers. A good visualization tells a story, removing the noise from data and highlighting the useful information. Effective data visualization is a delicate balancing act between form and function. In the field of data science, data visualization is undoubtedly the top word today. No matter what data you want to analyze, doing data visualization seems to be a necessary step. So, today I am going to take you through the definition, concept, implementation process and tools for data visualization. The challenge is to get the art right without getting the science wrong and vice versa. A data visualization first and foremost has to accurately convey the data. If one number is twice as large as another, but in the visualization, they look to be about the same, then the visualization is wrong. If a figure contains jarring colors, imbalanced visual elements, or other features that distract, then the viewer will find it harder to inspect the figure and interpret it correctly. While static charts and visualizations are undoubtedly useful, they make little use of the immense computing power that is readily available to us today. Interaction in visualization enables the fast exploration and discovery of data patterns that the user may not even have expected. It is also possible to reduce the amount of data shown at the same time, providing clearer visualizations, while still giving the user the option to get that information on demand at any time . Ben Shneiderman captured the role of interaction in his famous visual information seeking mantra ( Shneiderman , 1996 ) : overview first , zoom and filter , then details on demand . The best tools also can output an array of different chart , graph , and map types . Most of the tools below can output both images and interactive graphs . Some data visualization tools focus on a specific type of chart or map and do it very well . Google Charts is a powerful , free data visualization tool that is specifically for creating interactive charts for embedding online . It works with dynamic data and the outputs are based purely on HTML5 and SVG , so they work in browsers without the use of additional plugins. Data sources include Google Spreadsheets , Google Fusion Tables , Salesforce, and other SQL databases. There are a variety of chart types , including maps, scatter charts , column and bar charts , histograms , area charts, pie charts, treemaps, timelines , gauges , and many others . Users can create and distribute interactive and shareable dashboards , depicting trends , changes and densities of data in graphs and charts . Tableau can connect to files , relational data sources and big data sources to get and process data . Evaluation : Tableau is the simplest business intelligence tool in the desktop system. The art of making data beautiful is taking the world by storm . Data visualization experts and artists are creating amazing things in the world of data design every single day. Technically speaking, data visualization encompasses all things data art, infographics, and data dashboards. Such maps can be categorized as Thematic Cartography, which is a type of data visualization that presents and communicates specific data and information through a geographical illustration designed to show a particular theme connected with a specific geographic area. The earliest documented forms of data visualization were various thematic maps from different cultures and ideograms and hieroglyphs that provided and allowed interpretation of information illustrated. For example, Linear B tablets of Mycenae provided a visualization of information regarding the Late Bronze Age era trades in the Mediterranean. As this will be the professional skill in the coming days one must master this thing. Learn Data Science in the Best data science training institute in Hyderabad

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