更改

* 词条预计填充内容

* 词条预计填充内容

1.foundations 背景(了解的一些基础知识)；

1.foundations 背景(了解的一些基础知识)；<br>

2.术语内涵衍变（该术语如何产生及目前为止用法的一些不同）；

 

3.数据科学的研究内容

2.术语内涵衍变（该术语如何产生及目前为止用法的一些不同）；<br>

3.1数据科学基础理论

 

3.2 数据预处理

3.数据科学的研究内容<br>

3.3数据计算

 

3.4数据管理

3.1数据科学基础理论<br>

4.在数据科学方面的职业和工作；

 

5.数据科学的影响；

3.2 数据预处理<br>

6.数据科学中所涉及的一些技术和应用软件；  

 

7.数据科学、人工智能、机器学习之间的差别

3.3数据计算<br>

找到两篇博文供参考https://blog.csdn.net/fengdu78/article/details/105154546  https://blog.csdn.net/dev_csdn/article/details/79127658  

 

8.与统计学的关系  

3.4数据管理<br>

 

4.在数据科学方面的职业和工作；<br>

 

5.数据科学的影响；<br>

 

6.数据科学中所涉及的一些技术和应用软件；<br>

7.数据科学、人工智能、机器学习之间的差别<br>

 

找到两篇博文供参考https://blog.csdn.net/fengdu78/article/details/105154546  https://blog.csdn.net/dev_csdn/article/details/79127658 <br>

 

8.与统计学的关系 <br>

 

其中，第2部分是需要搜集补充的内容，第7部分有一些参考资料（后续还会再找一些），第8部分可进行补充。

其中，第2部分是需要搜集补充的内容，第7部分有一些参考资料（后续还会再找一些），第8部分可进行补充。

*任务分配

*任务分配

'''任务一：引言，1背景、2术语内涵、3研究内容'''

'''任务一：引言，1背景、2术语内涵、3研究内容'''

其中'''背景'''部分文字需要进行翻译；'''引言、术语内涵'''已有参考资料和初期的人工翻译文本，'''研究内容'''需要找到资料进行填充；

其中'''背景'''部分文字需要进行翻译；'''引言、术语内涵'''已有参考资料和初期的人工翻译文本，'''研究内容'''需要找到资料进行填充；<br>

 

'''任务二：4相关职业、5数据科学的影响'''

'''任务二：4相关职业、5数据科学的影响'''

其中并没有初期的人工翻译文本，可进一步搜集资料，使其更加完善完善；

其中并没有初期的人工翻译文本，可进一步搜集资料，使其更加完善完善；<br>

 

'''任务三：6相关应用软件、7与机器学习人工智能的差别、8与统计学的关系'''

'''任务三：6相关应用软件、7与机器学习人工智能的差别、8与统计学的关系'''

其中7、8需要搜集资料进行填充，8已有参考资料和初期的人工翻译文本；

其中7、8需要搜集资料进行填充，8已有参考资料和初期的人工翻译文本；<br>

 

*附言

*附言

== Foundations背景 ==

== Foundations背景 ==

== Etymology ==

== Etymology 术语词义衍变==

 

== Etymology ==

词源学

词源学

 

=== Modern usage ===

=== Modern usage ===

== Careers in data science ==

== Careers in data science 数据科学的相关职业==

Data science is a growing field. A career as a data scientist is ranked at the third best job in America for 2020 by Glassdoor, and was ranked the number one best job from 2016-2019.<ref>{{Cite web|url=https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm|title=Best Jobs in America|website=Glassdoor|language=en|access-date=2020-04-03}}</ref> Data scientists have a median salary of $118,370 per year or $56.91 per hour.<ref name=":2">{{Cite web|url=https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm|title=Computer and Information Research Scientists : Occupational Outlook Handbook: : U.S. Bureau of Labor Statistics|website=www.bls.gov|language=en-us|access-date=2020-04-03}}</ref> Job growth in this field is also above average, with a projected increase of 16% from 2018 to 2028.<ref name=":2" /> The largest employer of data scientists in the US is the federal government, employing 28% of the data science workforce.<ref name=":2" /> Other large employers of data scientists are computer system design services, research and development laboratories, and colleges and universities.<ref name=":2" /> Typically, data scientists work full time, and some work more than 40 hours a week.<ref name=":2" />

Data science is a growing field. A career as a data scientist is ranked at the third best job in America for 2020 by Glassdoor, and was ranked the number one best job from 2016-2019.<ref>{{Cite web|url=https://www.glassdoor.com/List/Best-Jobs-in-America-LST_KQ0,20.htm|title=Best Jobs in America|website=Glassdoor|language=en|access-date=2020-04-03}}</ref> Data scientists have a median salary of $118,370 per year or $56.91 per hour.<ref name=":2">{{Cite web|url=https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm|title=Computer and Information Research Scientists : Occupational Outlook Handbook: : U.S. Bureau of Labor Statistics|website=www.bls.gov|language=en-us|access-date=2020-04-03}}</ref> Job growth in this field is also above average, with a projected increase of 16% from 2018 to 2028.<ref name=":2" /> The largest employer of data scientists in the US is the federal government, employing 28% of the data science workforce.<ref name=":2" /> Other large employers of data scientists are computer system design services, research and development laboratories, and colleges and universities.<ref name=":2" /> Typically, data scientists work full time, and some work more than 40 hours a week.<ref name=":2" />

 

=== Educational path ===

=== Educational path ===

 

=== Specializations and associated careers ===

=== Specializations and associated careers ===

== Impacts of data science ==

== Impacts of data science数据科学的影响 ==

Big data is very quickly becoming a vital tool for businesses and companies of all sizes.<ref name=":5">{{Cite web|url=https://www.forbes.com/sites/peterpham/2015/08/28/the-impacts-of-big-data-that-you-may-not-have-heard-of/|title=The Impacts Of Big Data That You May Not Have Heard Of|last=Pham|first=Peter|website=Forbes|language=en|access-date=2020-04-03}}</ref> The availability and interpretation of big data has altered the business models of old industries and enabled the creation of new ones.<ref name=":5" /> Data-driven businesses are worth $1.2 trillion collectively in 2020, an increase from $333 billion in the year 2015.<ref name=":6">{{Cite web|url=https://towardsdatascience.com/how-data-science-will-impact-future-of-businesses-7f11f5699c4d|title=How Data Science will Impact Future of Businesses?|last=Martin|first=Sophia|date=2019-09-20|website=Medium|language=en|access-date=2020-04-03}}</ref> Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations.<ref name=":6" /> As big data continues to have a major impact on the world, data science does as well due to the close relationship between the two.<ref name=":6" />  

Big data is very quickly becoming a vital tool for businesses and companies of all sizes.<ref name=":5">{{Cite web|url=https://www.forbes.com/sites/peterpham/2015/08/28/the-impacts-of-big-data-that-you-may-not-have-heard-of/|title=The Impacts Of Big Data That You May Not Have Heard Of|last=Pham|first=Peter|website=Forbes|language=en|access-date=2020-04-03}}</ref> The availability and interpretation of big data has altered the business models of old industries and enabled the creation of new ones.<ref name=":5" /> Data-driven businesses are worth $1.2 trillion collectively in 2020, an increase from $333 billion in the year 2015.<ref name=":6">{{Cite web|url=https://towardsdatascience.com/how-data-science-will-impact-future-of-businesses-7f11f5699c4d|title=How Data Science will Impact Future of Businesses?|last=Martin|first=Sophia|date=2019-09-20|website=Medium|language=en|access-date=2020-04-03}}</ref> Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations.<ref name=":6" /> As big data continues to have a major impact on the world, data science does as well due to the close relationship between the two.<ref name=":6" />  

== Technologies and techniques ==

== Technologies and techniques 所涉及的技术和应用软件==

There are a variety of different technologies and techniques that are used for data science which depending on the application.

There are a variety of different technologies and techniques that are used for data science which depending on the application.

=== Techniques ===

=== Techniques ===

=== Technologies ===

=== Technologies ===

===Relationship to statistics===

==与机器学习、人工智能之间的异同==

 

==Relationship to statistics与统计学的关系==

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>

 

==References==

==References==