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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]
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.
Data science process flowchart from Doing Data Science, by Schutt & O'Neil (2013) Analysis refers to dividing a whole into its separate components for individual examination. [ 10 ] Data analysis is a process for obtaining raw data , and subsequently converting it into information useful for decision-making by users. [ 1 ]
They enable data analysis, mining, and artificial intelligence on a much larger scale than databases can allow, [20] and indeed data often flow from databases into data warehouses. [21] Business analysts , data engineers, and data scientists can access data warehouses using tools such as SQL or business intelligence software.
Data science process flowchart. John W. Tukey wrote the book Exploratory Data Analysis in 1977. [6] Tukey held that too much emphasis in statistics was placed on statistical hypothesis testing (confirmatory data analysis); more emphasis needed to be placed on using data to suggest hypotheses to test.
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 ...
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