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分析师也可能在不同的假设或情景下分析数据。例如,当分析师进行财务报表分析时,他们通常会根据不同的假设重新编制财务报表,以得出对未来现金流的估计,然后根据一定的利率贴现到现在的价值,以确定公司或其股票的估价。同样,国会预算办公室分析了各种政策选择对政府收入、支出和赤字的影响,为关键措施创造了可供选择的方案。
 
分析师也可能在不同的假设或情景下分析数据。例如,当分析师进行财务报表分析时,他们通常会根据不同的假设重新编制财务报表,以得出对未来现金流的估计,然后根据一定的利率贴现到现在的价值,以确定公司或其股票的估价。同样,国会预算办公室分析了各种政策选择对政府收入、支出和赤字的影响,为关键措施创造了可供选择的方案。
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==Other topics 其他主题==
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==其他主题==
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===Smart buildings 智能建筑===
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===智能建筑===
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A data analytics approach can be used in order to predict energy consumption in buildings.<ref name="Towards energy efficiency smart buildings models based on intelligent data analytics">{{cite journal
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数据分析方法可以用来预测建筑物的能源消耗。<ref name="Towards energy efficiency smart buildings models based on intelligent data analytics">{{cite journal
 
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A data analytics approach can be used in order to predict energy consumption in buildings.<ref name="Towards energy efficiency smart buildings models based on intelligent data analytics">{{cite journal
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数据分析方法可以用来预测建筑物的能源消耗。 <ref name="Towards energy efficiency smart buildings models based on intelligent data analytics">{ cite journal
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| last      = González-Vidal
 
| last      = González-Vidal
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| last      = González-Vidal
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| last      = González-Vidal
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| first      = Aurora
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| first      = Aurora
 
| first      = Aurora
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| last2      = Moreno-Cano
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| last2      = Moreno-Cano
 
| last2      = Moreno-Cano
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| first2    = Victoria
 
| first2    = Victoria
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| first2    = Victoria
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| date      = 2016
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| date      = 2016
 
| date      = 2016
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| title      = Towards energy efficiency smart buildings models based on intelligent data analytics
 
| title      = Towards energy efficiency smart buildings models based on intelligent data analytics
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| title      = Towards energy efficiency smart buildings models based on intelligent data analytics 基于智能数据分析的节能智能建筑模型
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| url        =  
 
| url        =  
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| url        =
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| journal    = Procedia Computer Science
 
| journal    = Procedia Computer Science
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| journal    = Procedia Computer Science
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| volume    = 83
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| volume    = 83
 
| volume    = 83
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| issue      = Elsevier
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| issue      = Elsevier
 
| issue      = Elsevier
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| pages      = 994–999
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| pages      = 994–999
 
| pages      = 994–999
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| doi        = 10.1016/j.procs.2016.04.213| doi-access= free
 
| doi        = 10.1016/j.procs.2016.04.213| doi-access= free
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| doi        = 10.1016/j.procs.2016.04.213| doi-access= free
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10.1016 / j.proc. 2016.04.213 | doi-access free
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}}
 
}}
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</ref>
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</ref> The different steps of the data analysis process are carried out in order to realise smart buildings, where the building management and control operations including heating, ventilation, air conditioning, lighting and security are realised automatically by miming the needs of the building users and optimising resources like energy and time.
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</ref> The different steps of the data analysis process are carried out in order to realise smart buildings, where the building management and control operations including heating, ventilation, air conditioning, lighting and security are realised automatically by miming the needs of the building users and optimising resources like energy and time.
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数据分析过程的不同步骤是为了实现智能建筑,建筑的管理和控制操作,包括'''供暖heating'''、'''通风ventilation'''、'''空调air conditioning'''、'''照明lighting'''和'''安保security''',都是通过模拟大厦使用者的需要和优化能源和时间等资源来自动实现的。
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</ref>
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数据分析过程的不同步骤是为了实现智能建筑,建筑的管理和控制操作,包括'''<font color='#ff8000'>供暖heating</font>'''、'''<font color='#ff8000'>通风ventilation</font>'''、'''<font color='#ff8000'>空调air conditioning</font>'''、'''<font color='#ff8000'>照明lighting</font>'''和'''<font color='#ff8000'>安保security</font>''',都是通过模拟大厦使用者的需要和优化能源和时间等资源来自动实现的。
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===分析和商业智能===
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分析是“广泛使用数据、统计和定量分析、解释和预测模型,以及基于事实的管理来驱动决策和行动。”它是商业智能的一个子集,而商业智能是一组使用数据来理解和分析商业表现的技术和流程。<ref name="Competing on Analytics 2007">{{cite book
===Analytics and business intelligence 分析和商业智能===
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{{Main|Analytics}}
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Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions." It is a subset of [[business intelligence]], which is a set of technologies and processes that use data to understand and analyze business performance.<ref name="Competing on Analytics 2007">{{cite book
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Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions." It is a subset of business intelligence, which is a set of technologies and processes that use data to understand and analyze business performance.<ref name="Competing on Analytics 2007">{{cite book
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<ref name="Competing on Analytics 2007">{ cite book
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| last = Davenport, Thomas and
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| last = Davenport, Thomas and
 
| last = Davenport, Thomas and
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| first = Harris, Jeanne
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| first = Harris, Jeanne
 
| first = Harris, Jeanne
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| year = 2007
 
| year = 2007
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| year = 2007
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| title = Competing on Analytics | publisher = O'Reilly
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| title = Competing on Analytics | publisher = O'Reilly
 
| title = Competing on Analytics | publisher = O'Reilly
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| isbn = 978-1-4221-0332-6| title-link = Competing on Analytics
 
| isbn = 978-1-4221-0332-6| title-link = Competing on Analytics
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| isbn = 978-1-4221-0332-6| title-link = Competing on Analytics
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| isbn 978-1-4221-0332-6 | title-link Competing on Analytics
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}}</ref>
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}}</ref>
 
}}</ref>
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===教育===
 
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{} / ref
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分析是“广泛使用数据、统计和定量分析、解释和预测模型,以及基于事实的管理来驱动决策和行动。”它是商业智能的一个子集,而商业智能是一组使用数据来理解和分析商业表现的技术和流程。
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===Education 教育===
      
[[File:User-activities.png|Analytic activities of data visualization users|thumb|right|350px]]
 
[[File:User-activities.png|Analytic activities of data visualization users|thumb|right|350px]]
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350px
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In [[education]], most educators have access to a [[data system]] for the purpose of analyzing student data.<ref>Aarons, D. (2009). [https://search.proquest.com/docview/202710770 Report finds states on course to build pupil-data systems.] ''Education Week, 29''(13), 6.</ref> These data systems present data to educators in an [[over-the-counter data]] format (embedding labels, supplemental documentation, and a help system and making key package/display and content decisions) to improve the accuracy of educators’ data analyses.<ref>Rankin, J. (2013, March 28). [https://sas.elluminate.com/site/external/recording/playback/link/table/dropin?sid=2008350&suid=D.4DF60C7117D5A77FE3AED546909ED2 How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help.] ''Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.''</ref>
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In education, most educators have access to a data system for the purpose of analyzing student data. These data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system and making key package/display and content decisions) to improve the accuracy of educators’ data analyses.
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在教育方面,大多数教育工作者都可以使用数据系统来分析学生的数据。这些数据系统以'''<font color='#ff8000'>场外交易数据格式over-the-counter data format</font>'''(嵌入标签、补充文件和帮助系统,并作出关键的包装 / 展示和内容决策)向教育工作者提供数据以提高其数据分析的准确性。
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在教育方面,大多数教育工作者都可以使用数据系统来分析学生的数据。<ref>Aarons, D. (2009). [https://search.proquest.com/docview/202710770 Report finds states on course to build pupil-data systems.] ''Education Week, 29''(13), 6.</ref> 这些数据系统以'''场外交易数据格式over-the-counter data format'''(嵌入标签、补充文件和帮助系统,并作出关键的包装 / 展示和内容决策)向教育工作者提供数据以提高其数据分析的准确性。<ref>Rankin, J. (2013, March 28). [https://sas.elluminate.com/site/external/recording/playback/link/table/dropin?sid=2008350&suid=D.4DF60C7117D5A77FE3AED546909ED2 How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help.] ''Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.''</ref>
    
==Practitioner notes 从业者注意事项==
 
==Practitioner notes 从业者注意事项==

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