BIG DATA ANALYTICS DATA MINING FOR ENGINEERS

This course focuses on the statistical analysis of data originating from engineering processes in order to allow the extraction of knowledge and to support decision-making actions. To this end, two classes of statistical models and methods are introduced: machine learning and data mining.
With machine learning we refer to algorithms and statistical models used to progressively improve their performance both in relation to a specific task assigned and in order to obtain forecasts without the need to be explicitly re-programmed. Data mining consists of a set of techniques and methodologies that have as their object the extraction of useful information from large volumes of digital data, both through automatic and semi-automatic methods.
In accordance with the modern paradigm of data-driven innovation, the course intends to introduce in a practical way the statistical methods and models necessary to face the upcoming challenges posed by complex systems and by the continuous technological change and business processes. This course also focuses on broad concepts, principles and techniques and establishes a baseline that can be applicable to any technological and industrial environment. The course is oriented to the practical analysis of engineering processes using KNIME software, as the main tool for statistical data analysis.