Business Statistics

The course of Business Statistics aims to provide the knowledge of the main Data Mining techniques addressed to the analysis of business data. Indeed, the increasing availability of data has brought out the need to deal with methodologies and tools for the quantitative decision-making processes in economic and business applications. Data may have a source within the firm, such as those related to customers or users, or may derive from appropriate market research. The presence of data of different nature (i.e., both qualitative and quantitative) requires that the student achieves suitable skills which allow him to justify the logical process underlying the adoption of a specific technical analysis, to formulate reasoning critically and rigorously, and to detect the synthetic information to support decisions, especially in the risk management process. The skills achieved in the course of Business Statistics will be useful for the courses whose main issues are related to marketing, market research, and decision-making processes.

At the end of the course, the student will have achieved the skills for both theoretical and practical formalization of Data Mining techniques presented along the course. In particular, the student will be able to: recognize the differences between supervised methods, non-supervised methods, descriptive models, and predictive models; demonstrate an adequate ability to choose the most suitable model based on the features of the available data and the purpose of the analysis to be led; select, among several models, the model characterized by the greatest predictive accuracy; learn tree models and provide a strong time series analysis. Moreover, the students will be able to implement the models using the programming language of the statistical R software; correctly interpret the analysis outputs by extracting information that can support the decision-making processes.