数据挖掘：数据挖掘是分析大量数据以发现关系、设计和洞察力的方法。 Witten 和 Eibeme 同意这些设计必须“有意义，因为它们带来了一些优势，通常是财务优势。”数据挖掘中的数据通常是定量的，特别是当我们考虑到社交媒体在很长一段时间后提供的数据呈指数级发展时，即大数据。
统计学：统计学是收集、组织、总结和分析数据以得出结论或回答问题的科学。在扩展中，测量是围绕在任何结论中给出一定程度的确定性。收集和分析大量数字信息的实践或科学，特别是为了从代表性测试中收集整体范围的原因。
下表列出了数据挖掘和统计之间的差异：
| Data Mining | Statistics |
|---|---|
| Data utilized is Numeric or Non numeric. | Data utilized is Numeric. |
| Inductive Process (Generation of modern hypothesis from data) | Deductive Process (Does not include making any forecasts) |
| Data Cleaning is drained data mining. | Clean data is utilized to apply statistical strategy. |
| Investigate and assemble data to begin with, builds show to distinguish patterns and make theories. | It gives speculations to test utilizing statistical. |
| Reasonable for expansive data sets | Suitable for littler data sets |
| Needs less client interaction to approve model thus, simple to automate. | Needs client interaction to approve show consequently, troublesome to automate. |
| It’s an calculation which learns from data without utilizing any programming rule. | Formalization of relationship in data within the shape of mathematical condition |
| Skills required for data mining are Classification, Clustering, Neural network, Association, Estimation, Sequence based analysis | Skills required for Statistics are Descriptive Statistical, Inferential Statistical |
| Applications are Financial Data Analysis, Retail Industry, Telecommunication Industry, | Applications are Demography, Actuarial ScienceBiostatistics, Quality Control |