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Data Analytics and Visualisation - PDF

   

Added on  2021-04-21

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Running head: DATA ANALYTICS AND VISUALISATIONData Analytics and VisualisationName of the Student:Name of the University:Author’s Note:

DATA ANALYTICS AND VISUALISATION1Table of ContentsIntroduction:...............................................................................................................................2Discussion:.................................................................................................................................2Conclusion:................................................................................................................................6References:.................................................................................................................................7

DATA ANALYTICS AND VISUALISATION2Introduction:A picture is worthier than thousand words, especially while some one is attempting tounderstand and gain insights from the data. For data analysis and decision making, somefactors are very crucial. These are data visualization, transformation of data, information andknowledge into visual representations. Data visualization is very crucial to the users as itcould suggest comprehensive insights for various analytical purpose. Commercial firms, non-governmental public sectors and other individual private companies require data visualisationwith the help of graphs and figures. The big data analytics has been spread in a large domainsalike marketing, finance, economy, politics and many others. The corporate houses utilize different types of software tools and packages such as R,Excel, Tableau and Qlikview for business decision making. The report aims to presentexplorations and enhancement in knowledge for comparing the cutting edges of techniques ofinformation visualization. For supporting effective decision making, the data visualizationtechnique is necessary. Sophisticated analyses and more effective decision making can beperformed by exploring data set.Big data quality relies up on three types of categories that are data qualitycategorization, data manager and big data handling. Discussion:It is the conventional method to generate graphs like Histogram, pie-chart, scatterplots, line-bar-bubble charts as well as trend charts. Sometimes data flow diagrams, entityrelationship (ER) diagrams and Venn diagrams are also used to be taken into consideration.However, in this era, the mainstream analytical visualisations are word/ text/ tag clouds,network diagrams, parallel coordinates, tree mapping, semantic networks and cone trees. The data visualization effectively increasing the solution for challenges as the abilityeffectively supporting to visualize the data set. Volume, variety and velocity are the three keyfeatures of big data analysis. The proper categorization of created, provoked, transactional,compiled, experimental or captured data is necessary for authentic visualization. The qualityof data effectively considers outliers and data outcomes. The factors that are pretty muchneeded for data visualization and interpretation are relevance, reliability, appropriateness andaccessibility. The advanced software such as Hadoop, Splunk, Python and D3 are the most

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