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Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.

Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.

'''<font color='#ff8000'>数据挖掘</font>'''是一种特殊的数据分析技术，侧重于统计建模和知识发现的预测目的（而不是纯粹的描述目的）。同时，商业智能涵盖了严重依赖于聚合的数据分析，主要侧重于商业信息。在统计应用中，数据分析可以分为'''<font color='#ff8000'>描述统计学descriptive statistics</font>'''、'''<font color='#ff8000'>探索性数据分析exploratory data analysis (EDA)</font>'''和'''<font color='#ff8000'>验证性数据分析confirmatory data analysis (CDA)</font>'''。EDA 侧重于发现数据中的新特征，而 CDA 侧重于确认或证伪现有的假设。'''<font color='#ff8000'>预测分析Predictive analytics</font>'''的重点是应用统计模型进行预测或分类，而'''<font color='#ff8000'>文本分析text analytics</font>'''则应用统计学、语言学和结构化技术从文本源中提取和分类信息（文本是一种'''<font color='#ff8000'>非结构化数据</font>'''）。以上是各种各样的数据分析。

'''<font color='#ff8000'>数据挖掘</font>'''是一种特殊的数据分析技术，侧重于统计建模和探索未来知识前景（而不仅仅是的描述行为目的）。同时，现代智能商业严重依赖于聚合的数据分析，尤其是在商业信息方面。在统计应用中，数据分析可以分为'''<font color='#ff8000'>描述统计学descriptive statistics</font>'''、'''<font color='#ff8000'>探索性数据分析exploratory data analysis (EDA)</font>'''和'''<font color='#ff8000'>验证性数据分析confirmatory data analysis (CDA)</font>'''。EDA 侧重于发现数据中的新特征，而 CDA 侧重于确认或证伪现有的假设。'''<font color='#ff8000'>预测分析Predictive analytics</font>'''的重点是应用统计模型进行预测或分类，而'''<font color='#ff8000'>文本分析text analytics</font>'''则应用统计学、语言学和结构化技术从文本源中提取和分类信息（文本是一种'''<font color='#ff8000'>非结构化数据</font>'''）。以上就是各种各样的数据分析。

Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination.

Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination.

'''<font color='#ff8000'>数据整合 Data integration</font>'''是数据分析的先驱，数据分析与'''<font color='#ff8000'>数据可视化data visualization</font>'''和'''<font color='#ff8000'>数据传播data dissemination</font>'''密切相关。

'''<font color='#ff8000'>数据整合 Data integration</font>'''是数据分析的先驱，数据分析与'''<font color='#ff8000'>数据可视化data visualization</font>'''同'''<font color='#ff8000'>数据传播data dissemination</font>'''密切相关。