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Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

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.

数据挖掘是一种特殊的数据分析技术，侧重于统计建模和知识发现，用于预测目的，而不是纯粹的描述目的，而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，数据分析可以分为描述统计学分析、探索性数据分析分析和验证性数据分析。Eda 侧重于发现数据中的新特征，而 CDA 侧重于确认或伪造现有的假设。预测分析的重点是应用统计模型进行预测预测或分类，而文本分析则应用统计学、语言学和结构化技术从文本来源中提取和分类信息，这是非结构化数据的一种。以上都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，侧重于统计建模和知识发现，用于预测目的，而不是纯粹的描述目的，而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'>描述统计学分析</font>'''、'''<font color='#ff8000'>探索性数据分析</font>'''和'''<font color='#ff8000'>验证性数据分析</font>'''。Eda 侧重于发现数据中的新特征，而 CDA 侧重于确认或伪造现有的假设。预测分析的重点是应用统计模型进行预测预测或分类，而'''<font color='#ff8000'>文本分析</font>'''则应用统计学、语言学和结构化技术从文本来源中提取和分类信息，这是'''<font color='#ff8000'>非结构化数据</font>'''的一种。以上都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.

Data science process flowchart from Doing Data Science, by Schutt&nbsp;& O'Neil (2013)

Data science process flowchart from Doing Data Science, by Schutt&nbsp;& O'Neil (2013)

数据科学处理流程图，来自《做数据科学》 ，Schutt & o’ neil (2013)

数据科学处理流程图，来自《'''<font color='#ff8000'>做数据科学</font>'''》 ，Schutt & o’ neil (2013)

Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a [[Process theory|process]] for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses or disprove theories.<ref name="Judd and McClelland 1989">{{cite book

Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a [[Process theory|process]] for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses or disprove theories.<ref name="Judd and McClelland 1989">{{cite book

When determining how to communicate the results, the analyst may consider data visualization techniques to help clearly and efficiently communicate the message to the audience. Data visualization uses information displays (such as tables and charts) to help communicate key messages contained in the data. Tables are helpful to a user who might look up specific numbers, while charts (e.g., bar charts or line charts) may help explain the quantitative messages contained in the data.

When determining how to communicate the results, the analyst may consider data visualization techniques to help clearly and efficiently communicate the message to the audience. Data visualization uses information displays (such as tables and charts) to help communicate key messages contained in the data. Tables are helpful to a user who might look up specific numbers, while charts (e.g., bar charts or line charts) may help explain the quantitative messages contained in the data.

在决定如何传达结果的时候，分析师可能会考虑数据可视化技术来帮助清晰有效地向听众传达信息。数据可视化使用信息显示(如表格和图表)来帮助传递包含在数据中的关键信息。表格对查找特定数字的用户很有帮助，而图表(例如柱状图或折线图)可以帮助解释数据中包含的定量信息。

在决定如何传达结果的时候，分析师可能会考虑'''<font color='#ff8000'>数据可视化</font>'''技术来帮助清晰有效地向听众传达信息。数据可视化使用信息显示(如表格和图表)来帮助传递包含在数据中的关键信息。表格对查找特定数字的用户很有帮助，而图表(例如柱状图或折线图)可以帮助解释数据中包含的定量信息。

Regression analysis may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y (e.g., "To what extent do changes in the unemployment rate (X) affect the inflation rate (Y)?"). This is an attempt to model or fit an equation line or curve to the data, such that Y is a function of X.

Regression analysis may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y (e.g., "To what extent do changes in the unemployment rate (X) affect the inflation rate (Y)?"). This is an attempt to model or fit an equation line or curve to the data, such that Y is a function of X.

当分析师试图确定自变量 x 对因变量 y 的影响程度时，可以使用回归分析分析法(例如，“失业率(x)的变化对通货膨胀率(y)的影响程度”) .这是一个试图模型或拟合一个方程线或曲线的数据，这样 y 是一个函数的 x。

当分析师试图确定自变量 x 对因变量 y 的影响程度时，可以使用'''<font color='#ff8000'>回归分析</font>'''分析法(例如，“失业率(x)的变化对通货膨胀率(y)的影响程度”) .这是一个试图模型或拟合一个方程线或曲线的数据，这样 y 是一个函数的 x。