Topic outline

  • General description

    This course presents the techniques most commonly employed in the analysis of large volumes of data, in the extraction of knowledge from this data, and in making decisions based on the knowledge acquired. It also presents the problems related to data mining that are not yet resolved satisfactorily at present and, therefore, are open research areas. Learning Objectives: § Collect and integrate data arising from different sources to build a data warehouse. § Know the different architectures and implementations of a data warehouse, as well as the guidelines to be followed in its maintenance. § Know the different types of problems (tasks) that can be addressed through data mining: classification, regression, clustering, correlations and association rules; and they will have criteria to discern which of these tasks must be resolved to address a real problem. § Know the different data mining techniques which can be used to solve the different tasks. § Be capable of obtaining new knowledge in the form of patterns or models from a data set. § Be capable of assessing the quality and interpretability of knowledge obtained by using different validation techniques