Differences between Data Mining and Predictive AnalyticsData mining is an integrated application in the Data Warehouse and describes a systematic process for pattern recognition in large data sets to identify conclusions and relationships. Using statistical methods, or genetic algorithms, data files can be automatically searched for statistical anomalies, patterns or rules.

Wikipedia defines as “Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.”

Data Analysis Techniques Every Manager Should Understand:

  • Correlation Analysis
  • Regression Analysis
  • Data Visualization
  • Scenario Analysis
  • Monte Carlo Simulation
  • Neural Networks
  • A/B Testing

 

Data Mining vs. Predictive Analytics – Are They the Same?

”Often data mining and predictive analytics used interchangeably. In fact, methods and tools of data mining play an essential role in predictive analytics solutions; but predictive analytics goes beyond data mining. For example, predictive analytics also uses text mining, on algorithms-based analysis method for unstructured contents such as articles, blogs, tweets, Facebook contents.”

 

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