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Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5] It uses techniques and theories drawn from many fields within the context of mathematics , statistics, computer science , information science , and domain knowledge . [ 6 ]
A free course can be "upgraded" to the paid version of a course, which includes instructor's feedback and grades for the submitted assignments, and (if the student gets a passing grade) a certificate of completion. [57] [60] Other Coursera courses, projects, specializations, etc. cannot be audited—they are only available in paid versions ...
A data science MOOC began in May 2013. [74] In May 2013, Coursera announced free e-books for some courses in partnership with Chegg, an online textbook-rental company. Students would use Chegg's e-reader, which limits copying and printing and could use the book only while enrolled in the class. [75]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
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.
According to Davenport, we now find ourselves in the third analytics era where companies of any industry are increasingly “competing on analytics”. The premise is that the many activities an Analytics 3.0 firm might conduct generate data trails that can be collected and subsequently analyzed.
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