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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. 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. 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. 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 imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.
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. 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 imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.
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]，是一个使用科学的方法、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。
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{{Cite journal
{{Cite journal
Simply Statistics.
Simply Statistics.
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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。
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<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巧和理论。
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。
[https://en.wikipedia.org/wiki/Turing_award 图灵奖]得主[https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist) 吉姆·格雷]（Jim Gray）将数据科学设想为一种科学的“第四范式”（[https://en.wikipedia.org/wiki/Empirical_research 经验主义]、[https://en.wikipedia.org/wiki/Basic_research 理论研究]、计算机辅助，现在是数据驱动），并且断言所有关于科学的事物由于信息技术和[https://en.wikipedia.org/wiki/Information_explosion 数据洪流]的影响在不断地发生改变。
[https://en.wikipedia.org/wiki/Turing_award 图灵奖]得主[https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist) 吉姆·格雷] Jim Gray 将数据科学设想为一种科学的“第四范式”（[https://en.wikipedia.org/wiki/Empirical_research 经验主义]、[https://en.wikipedia.org/wiki/Basic_research 理论研究]、计算机辅助，现在是数据驱动），并且断言所有关于科学的事物由于信息技术和[https://en.wikipedia.org/wiki/Information_explosion 数据洪流]的影响在不断地发生改变。
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{{cite book
{{cite book
，“数据科学”成了一个[https://en.wikipedia.org/wiki/Buzzword 流行术语]。它现在经常与早期概念互换使用，例如[https://en.wikipedia.org/wiki/Business_analytics 商业分析]
，“数据科学”成了一个[https://en.wikipedia.org/wiki/Buzzword 流行术语]。它现在经常与早期概念互换使用，例如[https://en.wikipedia.org/wiki/Business_analytics 商业分析]
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、[https://en.wikipedia.org/wiki/Business_intelligence 商业智能]、[https://en.wikipedia.org/wiki/Predictive_modelling 预测模型]和[https://en.wikipedia.org/wiki/Statistics 统计学]。“数据科学富有魅力”的观点甚至被汉斯·罗斯林（Hans Rosling）博士在2011年BBC纪录片中转述为“统计学是当今世界最具吸引力的学科。”内特·西尔弗（Nate Silver）
、[https://en.wikipedia.org/wiki/Business_intelligence 商业智能]、[https://en.wikipedia.org/wiki/Predictive_modelling 预测模型]和[https://en.wikipedia.org/wiki/Statistics 统计学]。“数据科学富有魅力”的观点甚至被汉斯·罗斯林 Hans Rosling博士在2011年BBC纪录片中转述为“统计学是当今世界最具吸引力的学科”。内特·西尔弗 Nate Silver
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则将数据科学描述为一种对于统计学家更具吸引力的词语。
在许多场合，为了博人眼球，一些早期的解决方案现在被简单地打上了“数据科学”的旗号，而这可能冲淡这个术语的效用。
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Warden, Pete(2011-05-09).
Warden, Pete(2011-05-09).
[https://www.kdnuggets.com/2018/05/data-science-4-reasons-failing-deliver.html "Data Science: 4 Reasons Why Most Are Failing to Deliver"]. ''www.kdnuggets.com''. Retrieved 2018-05-26.
[https://www.kdnuggets.com/2018/05/data-science-4-reasons-failing-deliver.html "Data Science: 4 Reasons Why Most Are Failing to Deliver"]. ''www.kdnuggets.com''. Retrieved 2018-05-26.
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