{"id":3562,"date":"2017-03-13T01:23:42","date_gmt":"2017-03-12T19:53:42","guid":{"rendered":"http:\/\/www.allerin.com\/blog\/?p=3562"},"modified":"2017-03-12T13:36:04","modified_gmt":"2017-03-12T08:06:04","slug":"how-data-scientists-differ-from-bi-data-analysts","status":"publish","type":"post","link":"https:\/\/www.allerin.com\/blog\/how-data-scientists-differ-from-bi-data-analysts\/","title":{"rendered":"How data scientists differ from BI data analysts?\u00a0"},"content":{"rendered":"

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As a discipline, Business Analytics has been around for 30 years, starting with the launch of MS Excel in 1985. Before this, data analytics was only limited to manual calculations using trial and error methods. There are two major trends that have contributed to the foundation of the Data Science phenomenon.\u00a0 First, the use of technology in various walks of life, particularly the use of Internet, led to an unprecedented data boom; the kind of information that is available to organizations now is nearly infinite. Secondly, new technologies made analyzing and interpreting such vast amounts of data possible and companies can now use all this data in decision-making. Before understanding about the difference between data scientists and data analysts, let\u2019s take a look at how the role of data scientist was born.<\/p>\n

Advent of the Data Scientist<\/h1>\n

Enterprises saw the availability of large volumes of data as a source of competitive advantage; \u00a0companies who could utilize this data effectively would take better decisions and would be ahead of the growth curve. To make sense out of such data, there arose a need for a new skill set that\u00a0 included the skill to draw customer\/user insights, business acumen, analytical abilities, programming skills, statistical skills, machine learning skills, data visualization, and much more. This led to the emergence of the role of a data scientist.<\/p>\n

Difference between Data Scientists and Data Analysts<\/h1>\n

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