Evolution of Analytics

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Analytics have evolved from manual analysis since the creation of papyrus to Excel in the 80's, pivot tables in the 90's and business intelligence in the new millennium. Are you ready to evolve?

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This paper is focused on the issues related to optimizing statistical approaches in the emerging fields of Computer Science and Information Technology. More emphasis has been given on the role of statistical techniques in modern data mining. Statistics is the science of learning from data and of measuring, controlling, and communicating uncertainty. Statistical approaches can play a vital role for providing significance contribution in the field of software engineering, neural network, data mining, bioinformatics and other allied fields. Statistical techniques not only helps make scientific models but it quantifies the reliability, reproducibility and general uncertainty associated with these models. In the current scenario, large amount of data is automatically recorded with computers and managed with the data base management systems (DBMS) for storage and fast retrieval purpose. The practice of examining large preexisting databases in order to generate new information is known as data mining. Presently, data mining has attracted substantial attention in the research and commercial arena which involves applications of a variety of statistical techniques. Twenty years ago mostly data was collected manually and the data set was in simple form but in present time, there have been considerable changes in the nature of data. Statistical techniques and computer applications can be utilized to obtain maximum information with the fewest possible measurements to reduce the cost of data collection.

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