Data Mining. Another name for Data Analysis.

Data Mining, or data mining, is a concept that first appeared in 1980 and is defined as the process of discovering patterns, trends and significant relationships in large data sets using statistical, mathematical and machine learning techniques. Patterns that are not visible to the naked eye and the human mind and that allow the discovery of relationships that would otherwise be hidden.

In 2000 this concept evolved as a result of an article “….Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics” in which the new concept of Data Science appeared. Both terms are interchangeable and in common parlance Data Mining has been losing strength and is now referred to as Data Science.

The Data Mining/Data Science process has the following stages:

1. Data Collection and PreparationInvolves the collection of data from various sources and its cleaning to ensure quality and consistency.
2. Data SelectionData selection: Determine the relevant data for each specific analysis.
3. Data TransformationConvert the data into a format suitable for analysis. This stage includes EDA (Exploratory Data Analytics) and FE (Feature Engineering).
4. ModelingModeling: Apply machine learning algorithms, such as decision trees, neural networks and clustering algorithms, to identify patterns and relationships.
5. Evaluation and ValidationVerify the accuracy and validity of the models created and the results obtained.
6. Interpretation and PresentationTranslation of results into understandable and useful information for decision making.

This general data analysis process is supported by three disciplines and three programming languages. Concepts and tools from these three disciplines are constantly used in all data analysis activities: Statistics, probability calculus and linear algebra. La aplicación de las mismas se hace a través de tres lenguaje sde programación (SQL, Python or R) and the use of their libraries.