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Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. [2] In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. [3]
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Analytics is the systematic computational analysis of data or statistics. [1] It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. [2] Analytics also entails applying data patterns toward effective decision-making.
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Data literacy is the ability to read, understand, create, and communicate data as information. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data. [1] It is, however, not similar to the ability to read text since it requires certain skills involving reading and understanding data. [2]
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Data processing is the collection and manipulation of digital data to produce meaningful information. [1] Data processing is a form of information processing , which is the modification (processing) of information in any manner detectable by an observer.
While many scientific investigations make use of data, statistics is generally concerned with the use of data in the context of uncertainty and decision-making in the face of uncertainty. [ 12 ] [ 13 ] Statistics is indexed at 62, a subclass of probability theory and stochastic processes, in the Mathematics Subject Classification. [ 14 ]