数据挖掘和数据分析的区别

1. 数据分析：
数据分析涉及数据的提取、清理、转换、建模和可视化，目的是提取重要和有用的信息，这些信息可以额外地有助于得出结论和做出选择。

数据分析的主要目的是在原始数据中寻找一些重要的信息，因此衍生的知识通常用于创建重要的选择。

2. 数据挖掘：
数据挖掘可以称为数据分析的一个子集。发现重要的规律和规律是对庞大知识的探索和分析。

数据挖掘也可以是识别和发现整个大数据集中隐藏模式和数据的系统和连续方法。此外，它用于构建机器学习模型，这些模型进一步用于人工智能。

下表列出了数据挖掘和数据分析之间的差异：

.差异表{
边框折叠：折叠；
宽度：100%；
}
.Difference-table td {
文字颜色：黑色！重要；
边框：1px 实心 #5fb962;
文本对齐：左！重要；
填充：8px；
}
.difference-table th {
边框：1px 实心 #5fb962;
填充：8px；
}
.差异表tr>th{
背景颜色：#c6ebd9；
垂直对齐：中间；
}
.Difference-table tr:nth-child(odd) {
背景色：#ffffff；
}

Based on	Data Mining	Data Analysis
Definition	It is the process of extracting important pattern from large datasets.	It is the process of analysing and organizing raw data in order to determine useful informations and decisions
Function	It is used in discovering hidden patterns in raw data sets .	In this all operations are involved in examining data sets to fine conclusions.
Data set	In this data set are generally large and structured.	Dataset can be large, medium or small, Also structured, semi structured, unstructured.
Models	Often require mathematical and stastical models	Analytical and business intelligence models
Visualization	It generally does not require visualization	Surely requires Data visualization.
Goal	Prime goal is to make data useable.	It is used to make data driven decisions.
Required Knowledge	It involves the intersection of machine learning, statistics and databases.	It requires the knowledge of computer science, statistics, mathematics, subject knowledge Al/Machine Learning.
Also known as	It is also known as Knowledge discovery in databases.	Data analysis can be divided into descriptive statistics,  exploratory data analysis, and confirmatory data analysis.
Output	It shows the data tends and patterns.	The output is verified or discarded hypothesis