CODE 60270 ACADEMIC YEAR 2025/2026 CREDITS 6 cfu anno 1 INGEGNERIA GESTIONALE 11956 (LM-31) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/05 LANGUAGE Italian TEACHING LOCATION GENOVA SEMESTER 2° Semester OVERVIEW The course illustrates the basic concepts of Business Analytics with particular reference to the approaches for statistical data modeling, diagnostic and predictive analytics, using methodologies based on machine learning for the solution of application problems and decision support in industrial, management and economics fields. AIMS AND CONTENT LEARNING OUTCOMES The course illustrates the basic concepts of Business Analytics with particular reference to the approaches for statistical data modeling, diagnostic and predictive analytics, using methodologies based on machine learning for the solution of application problems and decision support in industrial, management and economics fields. AIMS AND LEARNING OUTCOMES The student will acquire design skills of data analysis in industrial and management application fields. In particular, the student will be able to design a predictive analysis system and evaluate its performance. PREREQUISITES Basic knowledge of probability, statistics, analysis and data representation. Basic knowledge of Python or a similar programming language. TEACHING METHODS Lectures and computer assisted lab sessions. SYLLABUS/CONTENT Review of multivariate statistics and elements of decision theory Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics Supervised and unsupervised models Association Pattern Mining Cluster Analysis Rule-based methods and decision trees Kernel-based methods Elemsnts of neural networks Elements of methods for structured and semi-structured data Mehods for model evaluation Applications and case studies RECOMMENDED READING/BIBLIOGRAPHY Lecture notes provided during the course. Further readings: C.C.Aggarwal, Data mining: the textbook. Springer, 2015. M.J.Zaki, M.Wagner Jr., Data Mining and Machine Learning: Fundamental Concepts and Algorithms. Cambridge University Press, 2019. T.Hastie, R.Tibshirani, J.Friedman, The Elemsnts of Statistical Learning, Springer, 2009 (2nd Ed.) TEACHERS AND EXAM BOARD ANTONIO EMANUELE CINA' DAVIDE ANGUITA Ricevimento: By appointment. LESSONS LESSONS START https://corsi.unige.it/en/corsi/8734/studenti-orario Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Oral examination. The student will develop autonomously (individually or in cooperation with other students) a case study, selected among those proposed as exam topics and using the methods discussed during the course. The oral examination will focus on the discussion of the case study.
