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添加208字节 、 2020年5月9日 (六) 12:55
无编辑摘要
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* 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);
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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);
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3.数据科学的研究内容
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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
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3.2 数据预处理
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3.数据科学的研究内容<br>
3.3数据计算
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3.4数据管理
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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;
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5.数据科学的影响;
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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;  
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7.数据科学、人工智能、机器学习之间的差别
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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  
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8.与统计学的关系  
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3.4数据管理<br>
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4.在数据科学方面的职业和工作;<br>
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5.数据科学的影响;<br>
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6.数据科学中所涉及的一些技术和应用软件;<br>
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7.数据科学、人工智能、机器学习之间的差别<br>
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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>
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8.与统计学的关系 <br>
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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。
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*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;
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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>
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'''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;
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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>
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'''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;
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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>
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*附言
 
*附言
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== Foundations ==
      
== Foundations背景 ==
 
== Foundations背景 ==
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== Etymology ==
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== Etymology 术语词义衍变==
 
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== Etymology ==
      
词源学
 
词源学
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=== Modern usage ===
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=== Modern usage ===
 
=== Modern usage ===
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==研究内容==
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  --[[用户:趣木木|趣木木]]([[用户讨论:趣木木|讨论]])并不限于所列出来的条目 可以根据研究内容进行自主填充
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===数据科学基础理论===
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===数据预处理===
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===数据计算===
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===数据管理===
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== Careers in data science ==
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== Careers in data science 数据科学的相关职业==
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== Careers in data science ==
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数据科学的职业
      
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" />
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=== Educational path ===
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=== Educational path ===
 
=== Educational path ===
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=== Specializations and associated careers ===
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=== Specializations and associated careers ===
 
=== Specializations and associated careers ===
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== Impacts of data science ==
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== Impacts of data science ==
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== Impacts of data science数据科学的影响 ==
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数据科学的影响
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  --[[用户:趣木木|趣木木]]([[用户讨论:趣木木|讨论]])需要再进行补充 内容过少
    
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" />  
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== Technologies and techniques ==
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== Technologies and techniques ==
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== Technologies and techniques 所涉及的技术和应用软件==
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技术和技术
      
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.
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=== Techniques ===
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=== Techniques ===
      
=== Techniques ===
 
=== Techniques ===
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=== Technologies ===
      
=== Technologies ===
 
=== Technologies ===
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===Relationship to statistics===
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==与机器学习、人工智能之间的异同==
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==Relationship to statistics与统计学的关系==
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===Relationship to statistics===
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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>
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==References==
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==References==
 
==References==
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