Statistical Sciences

Learning goals Thanks to technological advances, the acquisition of data has become inexpensive and big data sets are easily obtained, for example, via Internet, e-commerce or by electronic banking services. Such data can be stored in data warehouses and data marts specifically intended to support business decisions. Data mining provides the tools to manage and analyse these data, to extract the relevant information and build forecasting models, fundamental tools in areas such as credit evaluation, marketing, customer relationship management. The course will examine the data preprocessing methods and their importance. We'll cover some of non-parametric models for classification and regression: decision trees, neural networks, support vector machines. Ensemble learning methods (Bagging, Boosting, Stacking, Blended) will be illustrated. The course will address also the analysis of textual data and images. Knowledge and understanding. Acquire the basics of data mining techniques. Understanding how and why to choose between alternative statistical methods, or possibly how to combine different methods. Ability to handle large amounts of data with the help of appropriate, commercial and open source, software. Applying knowledge and understanding. Students develop critical skills through the application of a wide range of statistical and machine learning models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Making judgements. Students develop critical skills through the application of a wide range of machine learning and statistical models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills. Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the intermediate and final written tests and in the oral tests. Communication skills are also developed through group activities. Learning skills. Students who pass the exam have learned a method of analysis that allows them to tackle, in subsequent statistical area teachings, the study of the formal properties of data mining procedures in more complex modeling contexts.