STATISTICS FOR BUSINESS DECISIONS

Course objectives

General objectives The course STATISTICS FOR BUSINESS DECISIONS provides students with the necessary knowledge to use data analysis to support business decisions. Through case studies, classroom exercises and group work, students will be able to acquire the main statistical techniques that will allow the treatment of the entire chain of multidimensional statistical analysis, from data collection to pre-treatment and to the final extraction of knowledge useful for decisions. Specific goals Knowledge and understanding: The course STATISTICS FOR BUSINESS DECISIONS provides students with the knowledge of data collection, cleaning and pre-treatment and the subsequent data analysis. The illustration of the theoretical arguments will proceed in parallel with the application to real data. In particular, the course allows the student to use some of the main data analysis techniques. Application skills: By mean of classroom exercises, case studies and group work, the course STATISTICS FOR BUSINESS DECISIONS supports students in the practical application of the main statistical techniques of data analysis for business decisions. At the end of the course, students will be able to formulate a research objective, to build a data matrix consistent with the objectives and to identify and implement the most suitable statistical methodology. Critical and judgment skills: The contents of the course allow the student to evaluate which reasearch questions can be achieved and how to achieve them. In particular, the course aims to develop skills in statistical data analysis. Communication skills: The course STATISTICS FOR BUSINESS DECISIONS aims to strengthen written and oral skills, with particular attention to the communication of the results of statistical data analysis for business decisions. Learning skills: The course shape student’s autonomy in the study and of one's own learning. This is also made possible through the execution of an applied project to be carried out in a group.

Channel 1
MARIA FELICE AREZZO Lecturers' profile

Program - Frequency - Exams

Course program
Data matrices and univariate analysis Data pre-treatment Graph and multidimansional data transformation Correspondence Analysis Distances and similarity measures Cluster analysis Classification Trees Random Forest Introduction to programming and to data analysis with R
Prerequisites
Basic statistics, introductory linear algebra and calculus.
Books
Analisi dei dati e data mining per le decisioni aziendali di Sergio Zani (Autore) Andrea Cerioli (Autore) Giuffrè, 2007
Frequency
Strongly adviced but not compulsory
Exam mode
Written exam
Lesson mode
Frontal teaching. Theoretical lessons followed by applied and illustrative exercises of the theory carried out with R.
  • Lesson code10600277
  • Academic year2025/2026
  • CourseBusiness Management
  • CurriculumMarketing
  • Year1st year
  • Semester2nd semester
  • SSDSECS-S/03
  • CFU9