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Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]
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. It is, however, not similar to the ability to read text since it requires certain skills involving reading and understanding data. [1]
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.
Social data scientists use both digitized data [22] (e.g. old books that have been digitized) and natively digital data (e.g. social media posts). [23] Since such data often take the form of found data that were originally produced for other purposes (commercial, governance, etc.) than research, data scraping, cleaning and other forms of preprocessing and data mining occupy a substantial part ...
When Harvard Business Review called data scientist "The Sexiest Job of the 21st Century" the term became a buzzword, [4] and is now often applied to business analytics, or even arbitrary use of data, or used as a term for statistics. While many university programs now offer a data science degree, there exists no consensus on a definition or ...
Research involves the collection and analysis of information and data with the intention of founding new knowledge and/or deciphering a new understanding of existing data. [42] Research ability is an analytical skill as it allows individuals to comprehend social implications. [ 40 ]
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