Data mining is sorting, organizing or grouping a large amount of data and extracting relevant information.

We could explain the term itself as the process of finding useful knowledge or information, that is, discovering knowledge from a large amount of data. Mining improves the decision-making process at the strategic-business level by providing insight into "hidden" data using business intelligence (BI) methodology. Mining also reveals relationships, logic, regularity and generally any structures among data. Mining involves organizing databases by cleaning data in order to access knowledge and acquire it based on the existing data in the databases. The development of technology, computers, and the Internet significantly contributes to the easier organization of data, but in order for them to become useful, it is necessary to transform them into information and knowledge.

The term mining is often equated with 2 different processes: knowledge discovery and knowledge prediction. The process of knowledge discovery implies the user's understanding of explicit information, for which it is important that it is in a readable form. Prediction refers to future events and in some approaches it can be readable and transparent while in others opaque.

With our guest speakers, we will go through the tools and processing methods used in data mining, as well as business applications and examples from practice.