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第156行： − 数据科学是一个跨学科的领域，致力于从数据集中提取知识，这些数据集通常都很大(请参阅[[大数据]])。<ref>+ − +
→Foundations背景
Data science is an interdisciplinary field focused on extracting knowledge from data sets, which are typically large (see [[big data]]).<ref>{{Cite web|url=http://www.datascienceassn.org/about-data-science|title=About Data Science {{!}} Data Science Association|website=www.datascienceassn.org|access-date=2020-04-03}}</ref> The field encompasses analysis, preparing data for analysis, and presenting findings to inform high-level decisions in an organization. As such, it incorporates skills from computer science, mathematics, statistics, [[information visualization]], graphic design, and business.<ref>{{Cite web|url=https://www.oreilly.com/library/view/doing-data-science/9781449363871/ch01.html|title=1. Introduction: What Is Data Science? - Doing Data Science [Book]|website=www.oreilly.com|language=en|access-date=2020-04-03}}</ref><ref>{{Cite web|url=https://medriscoll.com/post/4740157098/the-three-sexy-skills-of-data-geeks|title=the three sexy skills of data geeks|website=m.e.driscoll: data utopian|language=en|access-date=2020-04-03}}</ref> Statistician [[Nathan Yau]], drawing on [[Ben Fry]], also links data science to [[Human–computer interaction|human-computer interaction]]: users should be able to intuitively control and explore data.<ref>{{Cite web|url=https://flowingdata.com/2009/06/04/rise-of-the-data-scientist/|title=Rise of the Data Scientist|last=Yau|first=Nathan|date=2009-06-04|website=FlowingData|language=en|access-date=2020-04-03}}</ref><ref>{{Cite web|url=https://benfry.com/phd/dissertation/2.html|title=Basic Example|last=|first=|date=|website=benfry.com|url-status=live|archive-url=|archive-date=|access-date=2020-04-03}}</ref> In 2015, the [[American Statistical Association]] identified [[Database|database management]], statistics and [[machine learning]], and [[Distributed computing|distributed and parallel systems]] as the three emerging foundational professional communities.<ref>{{Cite web|url=https://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/|title=ASA Statement on the Role of Statistics in Data Science|date=2015-10-01|website=AMSTATNEWS|publisher=[[American Statistical Association]]|access-date=2019-05-29|archive-url=https://web.archive.org/web/20190620184935/https://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/|archive-date=20 June 2019|url-status=live}}</ref>
Data science is an interdisciplinary field focused on extracting knowledge from data sets, which are typically large (see [[big data]]).<ref>{{Cite web|url=http://www.datascienceassn.org/about-data-science|title=About Data Science {{!}} Data Science Association|website=www.datascienceassn.org|access-date=2020-04-03}}</ref> The field encompasses analysis, preparing data for analysis, and presenting findings to inform high-level decisions in an organization. As such, it incorporates skills from computer science, mathematics, statistics, [[information visualization]], graphic design, and business.<ref>{{Cite web|url=https://www.oreilly.com/library/view/doing-data-science/9781449363871/ch01.html|title=1. Introduction: What Is Data Science? - Doing Data Science [Book]|website=www.oreilly.com|language=en|access-date=2020-04-03}}</ref><ref>{{Cite web|url=https://medriscoll.com/post/4740157098/the-three-sexy-skills-of-data-geeks|title=the three sexy skills of data geeks|website=m.e.driscoll: data utopian|language=en|access-date=2020-04-03}}</ref> Statistician [[Nathan Yau]], drawing on [[Ben Fry]], also links data science to [[Human–computer interaction|human-computer interaction]]: users should be able to intuitively control and explore data.<ref>{{Cite web|url=https://flowingdata.com/2009/06/04/rise-of-the-data-scientist/|title=Rise of the Data Scientist|last=Yau|first=Nathan|date=2009-06-04|website=FlowingData|language=en|access-date=2020-04-03}}</ref><ref>{{Cite web|url=https://benfry.com/phd/dissertation/2.html|title=Basic Example|last=|first=|date=|website=benfry.com|url-status=live|archive-url=|archive-date=|access-date=2020-04-03}}</ref> In 2015, the [[American Statistical Association]] identified [[Database|database management]], statistics and [[machine learning]], and [[Distributed computing|distributed and parallel systems]] as the three emerging foundational professional communities.<ref>{{Cite web|url=https://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/|title=ASA Statement on the Role of Statistics in Data Science|date=2015-10-01|website=AMSTATNEWS|publisher=[[American Statistical Association]]|access-date=2019-05-29|archive-url=https://web.archive.org/web/20190620184935/https://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/|archive-date=20 June 2019|url-status=live}}</ref>
数据科学是一个跨学科的领域，致力于从数据集中提取知识，这些数据集通常都很大（请参阅[[大数据]]）。<ref>
{{Cite web|url=http://www.datascienceassn.org/about-data-science|title=About Data Science {{!}} Data Science Association|website=www.datascienceassn.org|access-date=2020-04-03}}
{{Cite web|url=http://www.datascienceassn.org/about-data-science|title=About Data Science {{!}} Data Science Association|website=www.datascienceassn.org|access-date=2020-04-03}}
</ref> 该领域包括分析，准备分析数据，及呈现结果，以此为组织的高层决策提供依据。因此，它融合了计算机科学、数学、统计学、信息可视化、图形设计和商业方面的技能。<ref>{{Cite web|url=https://www.oreilly.com/library/view/doing-data-science/9781449363871/ch01.html|title=1. Introduction: What Is Data Science? - Doing Data Science [Book]|website=www.oreilly.com|language=en|access-date=2020-04-03}}</ref><ref>{{Cite web|url=https://medriscoll.com/post/4740157098/the-three-sexy-skills-of-data-geeks|title=the three sexy skills of data geeks|website=m.e.driscoll: data utopian|language=en|access-date=2020-04-03}}
</ref> 该领域包括分析，准备分析数据，及呈现结果，以此为组织的高层决策提供依据。因此，它融合了计算机科学、数学、统计学、信息可视化、图形设计和商业方面的技能。<ref>{{Cite web|url=https://www.oreilly.com/library/view/doing-data-science/9781449363871/ch01.html|title=1. Introduction: What Is Data Science? - Doing Data Science [Book]|website=www.oreilly.com|language=en|access-date=2020-04-03}}</ref><ref>{{Cite web|url=https://medriscoll.com/post/4740157098/the-three-sexy-skills-of-data-geeks|title=the three sexy skills of data geeks|website=m.e.driscoll: data utopian|language=en|access-date=2020-04-03}}
</ref>统计学家丘南森（Nathan Yau）借鉴本•弗莱（Ben Fry）的观点，将数据科学和[[人机交互]]联系起来: 用户应该能够直观地控制和探索数据。<ref>
</ref>统计学家丘南森（Nathan Yau）借鉴本•弗莱（Ben Fry）的观点，将数据科学和[[人机交互]]联系起来: 用户应该能够直观地控制和探索数据。<ref>
{{Cite web|url=https://flowingdata.com/2009/06/04/rise-of-the-data-scientist/|title=Rise of the Data Scientist|last=Yau|first=Nathan|date=2009-06-04|website=FlowingData|language=en|access-date=2020-04-03}}</ref><ref>{{Cite web|url=https://benfry.com/phd/dissertation/2.html|title=Basic Example|last=|first=|date=|website=benfry.com|url-status=live|archive-url=|archive-date=|access-date=2020-04-03}}
{{Cite web|url=https://flowingdata.com/2009/06/04/rise-of-the-data-scientist/|title=Rise of the Data Scientist|last=Yau|first=Nathan|date=2009-06-04|website=FlowingData|language=en|access-date=2020-04-03}}</ref><ref>{{Cite web|url=https://benfry.com/phd/dissertation/2.html|title=Basic Example|last=|first=|date=|website=benfry.com|url-status=live|archive-url=|archive-date=|access-date=2020-04-03}}
</ref> 2015年，美国统计协会 the American Statistical Association 确定数据库管理、统计和机器学习，以及分布式和并行系统为三个新兴的基础专业社区。<ref>{{Cite web|url=https://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/|title=ASA Statement on the Role of Statistics in Data Science|date=2015-10-01|website=AMSTATNEWS|publisher=[[American Statistical Association]]|access-date=2019-05-29|archive-url=https://web.archive.org/web/20190620184935/https://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/|archive-date=20 June 2019|url-status=live}}</ref>
</ref> 2015年，美国统计协会（American Statistical Association）将数据库管理、统计和机器学习，以及分布式和并行系统确定为三个新兴的基础专业社区。<ref>{{Cite web|url=https://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/|title=ASA Statement on the Role of Statistics in Data Science|date=2015-10-01|website=AMSTATNEWS|publisher=[[American Statistical Association]]|access-date=2019-05-29|archive-url=https://web.archive.org/web/20190620184935/https://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/|archive-date=20 June 2019|url-status=live}}</ref>
== Etymology 术语词义衍变==
== Etymology 术语词义衍变==