Statistical Methods and Applications - Metodi statistici e applicazioni

General Managers worldwide, beyond their personal experience, rely more and more on the use of quantitative decision models which allow to take advantage of today’s data availability. Morover, new computational tools, including algorithms, cloud computing and distributed processing, make it possible to both develop and compute analytical models in a very short time, meeting the requirement of practical applications and often using real time data. Data Driven Decision Making is the new paradigm for managers to make better, evidence based, more rational, transparent and reliable decisions. In this context, the primary educational objective of the course is students' learning of the main decision problems that arise in real world and the quantitative methods to model them and to feed them with adequate data. Students must also be able to correctly use, for decision-making and management purposes, computer tools to analyze data generated by real problems in different contexts (e.g. service management, marketing, transportation, operations management and production, and finance) through the analysis of several case studies. Specific objectives a) Knowledge and ability to understand After attending the course the students know and classify the main decision problems arising in real world organization and the main analytical methods (decision and optimization models and algorithms) to be used to support a Manager during his/her decision process. b) Ability to apply knowledge and understanding At the end of the course the students are able to formalize real problems in terms of decision problems and to apply the specific methods taught in the course to solve them. They are also able to classify the type of problem to it the most appropriate quantitative method, experimenting the effectiveness for decisional purposes also on real problems. c) Autonomy of judgment Students develop critical skills through the application of modeling, decision analysis and multi objective optimization methodologies to a broad set of practical problems. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using methods of analysis and realistic scenarios different from each other. They learn to critically interpret the results obtained by applying the procedures to real data sets. d) Communication skills Students, through the study and the carrying out of practical exercises, acquire the technical- scientific language of the course, which must be properly used both in the intermediate and final written tests and in the oral tests. Communication skills are also developed through group activities. e) Learning ability Students who pass the exam have learned methods of decision analysis and multiobjective optimization that allow them to face, decision-making problems and optimization on complex organizations.