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Many statisticians, including [[Nate Silver]], have argued that data science is not a new field, but rather another name for statistics.<ref>{{Cite web|url=https://www.statisticsviews.com/details/feature/5133141/Nate-Silver-What-I-need-from-statisticians.html|title=Nate Silver: What I need from statisticians - Statistics Views|website=www.statisticsviews.com|access-date=2020-04-03}}</ref> Others argue that data science is distinct from statistics because it focuses on problems and techniques unique to digital data.<ref>{{Cite web|url=http://priceonomics.com/whats-the-difference-between-data-science-and/|title=What's the Difference Between Data Science and Statistics?|website=Priceonomics|language=en|access-date=2020-04-03}}</ref> [[Vasant Dhar]] writes that statistics emphasizes quantitative data and description. In contrast, data science deals with quantitative and qualitative data (e.g. images) and emphasizes prediction and action.<ref>{{Cite journal|last=DharVasant|date=2013-12-01|title=Data science and prediction|journal=Communications of the ACM|volume=56|issue=12|pages=64–73|language=EN|doi=10.1145/2500499}}</ref> [[Andrew Gelman]] of Columbia University and data scientist Vincent Granville have described statistics as a nonessential part of data science.<ref>{{Cite web|url=https://statmodeling.stat.columbia.edu/2013/11/14/statistics-least-important-part-data-science/|title=Statistics is the least important part of data science « Statistical Modeling, Causal Inference, and Social Science|website=statmodeling.stat.columbia.edu|access-date=2020-04-03}}</ref><ref>{{Cite web|url=https://www.datasciencecentral.com/profiles/blogs/data-science-without-statistics-is-possible-even-desirable|title=Data science without statistics is possible, even desirable|last=Posted by Vincent Granville on December 8|first=2014 at 5:00pm|last2=Blog|first2=View|website=www.datasciencecentral.com|language=en|access-date=2020-04-03}}</ref>

Many statisticians, including [[Nate Silver]], have argued that data science is not a new field, but rather another name for statistics.<ref>{{Cite web|url=https://www.statisticsviews.com/details/feature/5133141/Nate-Silver-What-I-need-from-statisticians.html|title=Nate Silver: What I need from statisticians - Statistics Views|website=www.statisticsviews.com|access-date=2020-04-03}}</ref> Others argue that data science is distinct from statistics because it focuses on problems and techniques unique to digital data.<ref>{{Cite web|url=http://priceonomics.com/whats-the-difference-between-data-science-and/|title=What's the Difference Between Data Science and Statistics?|website=Priceonomics|language=en|access-date=2020-04-03}}</ref> [[Vasant Dhar]] writes that statistics emphasizes quantitative data and description. In contrast, data science deals with quantitative and qualitative data (e.g. images) and emphasizes prediction and action.<ref>{{Cite journal|last=DharVasant|date=2013-12-01|title=Data science and prediction|journal=Communications of the ACM|volume=56|issue=12|pages=64–73|language=EN|doi=10.1145/2500499}}</ref> [[Andrew Gelman]] of Columbia University and data scientist Vincent Granville have described statistics as a nonessential part of data science.<ref>{{Cite web|url=https://statmodeling.stat.columbia.edu/2013/11/14/statistics-least-important-part-data-science/|title=Statistics is the least important part of data science « Statistical Modeling, Causal Inference, and Social Science|website=statmodeling.stat.columbia.edu|access-date=2020-04-03}}</ref><ref>{{Cite web|url=https://www.datasciencecentral.com/profiles/blogs/data-science-without-statistics-is-possible-even-desirable|title=Data science without statistics is possible, even desirable|last=Posted by Vincent Granville on December 8|first=2014 at 5:00pm|last2=Blog|first2=View|website=www.datasciencecentral.com|language=en|access-date=2020-04-03}}</ref>

包括'''纳特•西尔弗 Nate Silver''' 在内的许多统计学家都认为，数据科学不是一个新领域，而是统计的另一个名称。其他人则认为，数据科学与统计学不同，因为它关注的是数字数据特有的问题和技术。'''瓦桑特·达尔 Vasant Dhar'''写道，统计学强调定量数据和描述。相比之下，数据科学处理定量和定性数据(如图像)，强调预测和行动。哥伦比亚大学的'''安德鲁·格尔曼 Andrew Gelman''' 和数据科学家'''文森特·格兰维尔 Vincent Granville'''将统计描述为数据科学中一个不重要的部分。

包括'''纳特•西尔弗（Nate Silver）''' 在内的许多统计学家都认为，数据科学不是一个新领域，而是统计学的另一个名称。其他人则认为，数据科学与统计学不同，因为它关注的是数字化数据特有的问题和技术。'''瓦桑特·达尔（Vasant Dhar）'''写道，统计学强调定量的数据和描述。相比之下，数据科学处理定量和定性的数据(例如，图像)，并强调预测和行动。哥伦比亚大学的'''安德鲁·格尔曼（Andrew Gelman）''' 和数据科学家'''文森特·格兰维尔（Vincent Granville）'''将统计学描述为数据科学中一个不重要的部分。

Stanford professor [[David Donoho]] writes that data science is not distinguished from statistics by the size of datasets or use of computing, and that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program. He describes data science as an applied field growing out of traditional statistics.<ref name=":7" />  

Stanford professor [[David Donoho]] writes that data science is not distinguished from statistics by the size of datasets or use of computing, and that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program. He describes data science as an applied field growing out of traditional statistics.<ref name=":7" />  

斯坦福大学教授 '''大卫·林奇  David Donoho''' 写道，数据科学与统计学之间并不存在数据集的大小或计算机的使用，许多研究生课程错误地宣传他们的分析学和统计学训练是数据科学课程的本质。他把数据科学描述为从传统统计学中发展出来的一个应用领域。

斯坦福大学教授 '''大卫·多诺霍（David Donoho）''' 写道，数据科学与统计学的区别不在于数据集的大小或计算的使用，许多研究生课程误导性地将他们的分析与统计培训宣传为数据科学课程的核心。他把数据科学描述为从传统统计学中发展出来的一个应用领域。

在[https://en.wikipedia.org/wiki/American_Statistical_Association 美国统计协会]的联合统计学会议上发表主旨演说后的问答部分，著名应用统计学家[https://en.wikipedia.org/wiki/Nate_Silver 内特·西尔弗]（Nate Silver）说道：“我认为数据科学家对于统计学家是一个富有魅力的词语…统计学是科学的一条分支。数据科学家在某种意义上略显多余，而且人们不应该痛斥统计学家这个词。”

在[https://en.wikipedia.org/wiki/American_Statistical_Association 美国统计协会]的联合统计学会议上发表主旨演说后的问答部分，著名应用统计学家[https://en.wikipedia.org/wiki/Nate_Silver 纳特·西尔弗]（Nate Silver）说道：“我认为数据科学家对于统计学家是一个富有魅力的词语…统计学是科学的一条分支。数据科学家在某种意义上略显多余，而且人们不应该痛斥统计学家这个词。”

<ref name="NateSilver">

<ref name="NateSilver">

[http://www.statisticsviews.com/details/feature/5133141/Nate-Silver-What-I-need-from-statisticians.html "Nate Silver: What I need from statisticians"]. 23 Aug 2013  

[http://www.statisticsviews.com/details/feature/5133141/Nate-Silver-What-I-need-from-statisticians.html "Nate Silver: What I need from statisticians"]. 23 Aug 2013  

类似地，就像许多其他数据科学学界支持者一样，

类似地，就像许多其他数据科学学界支持者一样，

<ref name=":1" />

<ref name=":1" />

[https://en.wikipedia.org/wiki/New_York_University 纽约大学][https://en.wikipedia.org/wiki/NYU_Stern_Center_for_Business_and_Human_Rights 斯特恩商学院]的瓦桑德·达尔（Vasant Dhar）在2013年12月更加明确地表示数据科学与现存的仅仅聚焦于解释[https://en.wikipedia.org/wiki/Data_set 数据集]的横跨所有[https://en.wikipedia.org/wiki/Discipline_(academia) 学科]的数据分析实践不同。数据科学为[https://en.wikipedia.org/wiki/Predictive_modelling 预测模型]寻求了可行和一致的[https://en.wikipedia.org/wiki/Pattern_recognition 模式]。

[https://en.wikipedia.org/wiki/New_York_University 纽约大学][https://en.wikipedia.org/wiki/NYU_Stern_Center_for_Business_and_Human_Rights 斯特恩商学院]的瓦桑特·达尔（Vasant Dhar）在2013年12月更加明确地表示数据科学与现存的仅仅聚焦于解释[https://en.wikipedia.org/wiki/Data_set 数据集]的横跨所有[https://en.wikipedia.org/wiki/Discipline_(academia) 学科]的数据分析实践不同。数据科学为[https://en.wikipedia.org/wiki/Predictive_modelling 预测模型]寻求了可行和一致的[https://en.wikipedia.org/wiki/Pattern_recognition 模式]。

<ref name=":0" />

<ref name=":0" />

这项实际的工程目标采用了超越了传统[https://en.wikipedia.org/wiki/Analytics 数据分析]的数据科学。如今这些学科和[https://en.wikipedia.org/wiki/Applied_science 应用领域]的数据缺乏可靠[https://en.wikipedia.org/wiki/Theory 理论]以供形成有力的预测模型，就像[https://en.wikipedia.org/wiki/Health_science 健康科学]和[https://en.wikipedia.org/wiki/Social_science 社会科学]那样。

这项实际的工程目标采用了超越了传统[https://en.wikipedia.org/wiki/Analytics 数据分析]的数据科学。如今这些学科和[https://en.wikipedia.org/wiki/Applied_science 应用领域]的数据缺乏可靠[https://en.wikipedia.org/wiki/Theory 理论]以供形成有力的预测模型，就像[https://en.wikipedia.org/wiki/Health_science 健康科学]和[https://en.wikipedia.org/wiki/Social_science 社会科学]那样。