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'''Data science''' is an [[inter-disciplinary]] field that uses scientific methods, processes, algorithms and systems to extract [[knowledge]] and insights from many structural and [[unstructured data]].<ref>{{Cite journal | last1 = Dhar | first1 = V. | title = Data science and prediction | doi = 10.1145/2500499 | journal = Communications of the ACM | volume = 56 | issue = 12 | pages = 64–73 | year = 2013 | pmid =  | pmc =  | url = http://cacm.acm.org/magazines/2013/12/169933-data-science-and-prediction/fulltext | access-date = 2 September 2015 | archive-url = https://web.archive.org/web/20141109113411/http://cacm.acm.org/magazines/2013/12/169933-data-science-and-prediction/fulltext | archive-date = 9 November 2014 | url-status = live }}</ref><ref>{{cite web | url=http://simplystatistics.org/2013/12/12/the-key-word-in-data-science-is-not-data-it-is-science/ | title=The key word in "Data Science" is not Data, it is Science | publisher=Simply Statistics | date=2013-12-12 | author=[[Jeffrey T. Leek|Jeff Leek]] | access-date=1 January 2014 | archive-url=https://web.archive.org/web/20140102194117/http://simplystatistics.org/2013/12/12/the-key-word-in-data-science-is-not-data-it-is-science/ | archive-date=2 January 2014 | url-status=live }}</ref> Data science is related to [[data mining]] and [[big data]].

'''Data science''' is an [[inter-disciplinary]] field that uses scientific methods, processes, algorithms and systems to extract [[knowledge]] and insights from many structural and [[unstructured data]].<ref>{{Cite journal | last1 = Dhar | first1 = V. | title = Data science and prediction | doi = 10.1145/2500499 | journal = Communications of the ACM | volume = 56 | issue = 12 | pages = 64–73 | year = 2013 | pmid =  | pmc =  | url = http://cacm.acm.org/magazines/2013/12/169933-data-science-and-prediction/fulltext | access-date = 2 September 2015 | archive-url = https://web.archive.org/web/20141109113411/http://cacm.acm.org/magazines/2013/12/169933-data-science-and-prediction/fulltext | archive-date = 9 November 2014 | url-status = live }}</ref><ref>{{cite web | url=http://simplystatistics.org/2013/12/12/the-key-word-in-data-science-is-not-data-it-is-science/ | title=The key word in "Data Science" is not Data, it is Science | publisher=Simply Statistics | date=2013-12-12 | author=[[Jeffrey T. Leek|Jeff Leek]] | access-date=1 January 2014 | archive-url=https://web.archive.org/web/20140102194117/http://simplystatistics.org/2013/12/12/the-key-word-in-data-science-is-not-data-it-is-science/ | archive-date=2 January 2014 | url-status=live }}</ref> Data science is related to [[data mining]] and [[big data]]. Data science is a "concept to unify [[statistics]], [[data analysis]], [[machine learning]] and their related methods" in order to "understand and analyze actual phenomena" with data.<ref>{{Cite book|chapter-url=https://www.springer.com/book/9784431702085|title=Data Science, Classification, and Related Methods|last=Hayashi|first=Chikio|date=1998-01-01|publisher=Springer Japan|isbn=9784431702085|editor-last=Hayashi|editor-first=Chikio|series=Studies in Classification, Data Analysis, and Knowledge Organization|location=|pages=40–51|language=en|chapter=What is Data Science? Fundamental Concepts and a Heuristic Example|doi=10.1007/978-4-431-65950-1_3|editor-last2=Yajima|editor-first2=Keiji|editor-last3=Bock|editor-first3=Hans-Hermann|editor-last4=Ohsumi|editor-first4=Noboru|editor-last5=Tanaka|editor-first5=Yutaka|editor-last6=Baba|editor-first6=Yasumasa}}</ref> It uses techniques and theories drawn from many fields within the context of [[mathematics]], [[statistics]], [[computer science]], and [[information science]]. [[Turing award]] winner [[Jim Gray (computer scientist)|Jim Gray]] imagined data science as a "fourth paradigm" of science ([[Empirical research|empirical]], [[Basic research|theoretical]], [[computational science|computational]] and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the [[information explosion|data deluge]].<ref name="TansleyTolle2009">{{cite book|author1=Stewart Tansley|author2=Kristin Michele Tolle|title=The Fourth Paradigm: Data-intensive Scientific Discovery|url=https://books.google.com/?id=oGs_AQAAIAAJ|year=2009|publisher=Microsoft Research|isbn=978-0-9825442-0-4|access-date=16 December 2016|archive-url=https://web.archive.org/web/20170320193019/https://books.google.com/books?id=oGs_AQAAIAAJ|archive-date=20 March 2017|url-status=live}}</ref><ref name="BellHey2009">{{cite journal|last1=Bell|first1=G.|last2=Hey|first2=T.|last3=Szalay|first3=A.|title=COMPUTER SCIENCE: Beyond the Data Deluge|journal=Science|volume=323|issue=5919|year=2009|pages=1297–1298|issn=0036-8075|doi=10.1126/science.1170411|pmid=19265007}}</ref>

 

 

 

 

 

Data science is a "concept to unify [[statistics]], [[data analysis]], [[machine learning]] and their related methods" in order to "understand and analyze actual phenomena" with data.<ref>{{Cite book|chapter-url=https://www.springer.com/book/9784431702085|title=Data Science, Classification, and Related Methods|last=Hayashi|first=Chikio|date=1998-01-01|publisher=Springer Japan|isbn=9784431702085|editor-last=Hayashi|editor-first=Chikio|series=Studies in Classification, Data Analysis, and Knowledge Organization|location=|pages=40–51|language=en|chapter=What is Data Science? Fundamental Concepts and a Heuristic Example|doi=10.1007/978-4-431-65950-1_3|editor-last2=Yajima|editor-first2=Keiji|editor-last3=Bock|editor-first3=Hans-Hermann|editor-last4=Ohsumi|editor-first4=Noboru|editor-last5=Tanaka|editor-first5=Yutaka|editor-last6=Baba|editor-first6=Yasumasa}}</ref> It uses techniques and theories drawn from many fields within the context of [[mathematics]], [[statistics]], [[computer science]], and [[information science]]. [[Turing award]] winner [[Jim Gray (computer scientist)|Jim Gray]] imagined data science as a "fourth paradigm" of science ([[Empirical research|empirical]], [[Basic research|theoretical]], [[computational science|computational]] and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the [[information explosion|data deluge]].<ref name="TansleyTolle2009">{{cite book|author1=Stewart Tansley|author2=Kristin Michele Tolle|title=The Fourth Paradigm: Data-intensive Scientific Discovery|url=https://books.google.com/?id=oGs_AQAAIAAJ|year=2009|publisher=Microsoft Research|isbn=978-0-9825442-0-4|access-date=16 December 2016|archive-url=https://web.archive.org/web/20170320193019/https://books.google.com/books?id=oGs_AQAAIAAJ|archive-date=20 March 2017|url-status=live}}</ref><ref name="BellHey2009">{{cite journal|last1=Bell|first1=G.|last2=Hey|first2=T.|last3=Szalay|first3=A.|title=COMPUTER SCIENCE: Beyond the Data Deluge|journal=Science|volume=323|issue=5919|year=2009|pages=1297–1298|issn=0036-8075|doi=10.1126/science.1170411|pmid=19265007}}</ref>

数据科学类似于[https://en.wikipedia.org/wiki/Data_mining 数据挖掘]，是一个通过科学的方法、过程、算法和系统，从有结构或无结构的各种形式的[https://en.wikipedia.org/wiki/Data 数据]中提炼[https://en.wikipedia.org/wiki/Knowledge 知识]和见解的跨学科领域。

数据科学类似于[https://en.wikipedia.org/wiki/Data_mining 数据挖掘]，是一个通过科学的方法、过程、算法和系统，从有结构或无结构的各种形式的[https://en.wikipedia.org/wiki/Data 数据]中提炼[https://en.wikipedia.org/wiki/Knowledge 知识]和见解的跨学科领域。

数据科学的概念结合了统计学、数据分析、机器学习等相关方法，以便于借助数据理解和分析实际现象。

数据科学的概念结合了统计学、数据分析、机器学习等相关方法，以便于借助数据理解和分析实际现象。

<ref name="Hayashi" />

<ref name="Hayashi" />

它使用了来自[https://en.wikipedia.org/wiki/Mathematics 数学]、[https://en.wikipedia.org/wiki/Statistics 统计学]、[https://en.wikipedia.org/wiki/Information_science 信息科学]、[https://en.wikipedia.org/wiki/Computer_science 计算机科学]等许多学科领域的技术与理论。

它使用了从[https://en.wikipedia.org/wiki/Mathematics 数学]、[https://en.wikipedia.org/wiki/Statistics 统计学]、[https://en.wikipedia.org/wiki/Information_science 信息科学]、[https://en.wikipedia.org/wiki/Computer_science 计算机科学]等许多学科领域获得的技术与理论。