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→Exploratory data analysis 探索性数据分析
Once the data are cleaned, it can be analyzed. Analysts may apply a variety of techniques referred to as exploratory data analysis to begin understanding the messages contained in the data. The process of exploration may result in additional data cleaning or additional requests for data, so these activities may be iterative in nature. Descriptive statistics, such as the average or median, may be generated to help understand the data. Data visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data.
Once the data are cleaned, it can be analyzed. Analysts may apply a variety of techniques referred to as exploratory data analysis to begin understanding the messages contained in the data. The process of exploration may result in additional data cleaning or additional requests for data, so these activities may be iterative in nature. Descriptive statistics, such as the average or median, may be generated to help understand the data. Data visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data.
数据被筛选之后就可以进行分析。分析者可能会运用各种被称为探索性数据分析的技术着手理解数据中包含的信息。发现的过程可能导致额外的数据筛选或数据请求，因此这些活动可能具有迭代性质。在描述统计学量，例如平均值或中位数时，就可以用来帮助理解数据。数据可视化还可以被用于检查图形格式的数据，以获得关于数据中的消息的额外的洞察力。
===Modeling and algorithms 建模和算法===
===Modeling and algorithms 建模和算法===