{"id":359,"date":"2019-04-12T10:24:06","date_gmt":"2019-04-12T04:54:06","guid":{"rendered":"https:\/\/editor.eduplusnow.com\/?p=359"},"modified":"2019-04-12T10:24:06","modified_gmt":"2019-04-12T04:54:06","slug":"why-data-scientists-should-embrace-graphic-analysis","status":"publish","type":"post","link":"https:\/\/blog.eduplusnow.com\/blog\/why-data-scientists-should-embrace-graphic-analysis\/","title":{"rendered":"Why Data Scientists Should Embrace Graphic Analysis"},"content":{"rendered":"

Why Data Scientists Should Embrace Graphic Analysis<\/h1>\n
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Visualizing data has become an indispensable art and practice that has proven instrumental in bridging the gap between comprehension of the human mind and digitized interpretation of a computer, thus, drawing meaningful analytical insights from mounds of data. Data scientists, who straddle the worlds of business and IT using technical and analytical skills to find concrete answers to complex questions turning a mix of mathematics and computer science to predict trends can leverage the ease and efficiency brought into the process by graphic analysis. This becomes especially pertinent in the present scenario where every business establishment, big or small, is dealing with data. Newer, more innovative ways are continually being explored to make data analytics easier and more meaningful. As of today, graphic analysis, is leading the way vis-a-vis technique innovation for filtering out optimum meaningful insights from an existing data pool.<\/p>\n

What is Graphic Analysis?<\/strong><\/h5>\n
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Graphic analysis or graph analytics, also commonly referred to as network analysis, is a relatively new and upcoming area of data analytics. One of the main reasons behind graphic analysis\u2019 sudden rise to popularity in the data analytics circles is its ability to accurately process social network influencer analysis. That\u2019s an area marketing professionals and business owners are definitely interested in, given its potential to create well targeted campaigns capable to going viral on the internet, thus, driving sales.<\/p>\n

Graphic analysis goes beyond the realm of simple data visualizations, and works with edges and nodes, referred to as vertices in mathematical terms. In the world of analytics, these nodes can represent anything from customers to employees, buildings, cities, twitter handles, web pages, etc. can represent parameters such as likes, dislikes, interactions, engagement and so on. So, graphic analysis works on establishing patterns, commonalities and co-relations between two very different variables.<\/p>\n

While use of graph analytics for driving business growth is emerging as its primary application, several other potential uses of this technique exist:<\/p>\n