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BUSINESS ANALYTICS PROGRAM

Overview

This course provides a general introduction to the methods and tools of Business Analytics, with the goal of bringing students closer to data analysis in real-world contexts. Through an application-oriented approach, the course promotes an integrated view of data as a strategic resource for decision support in business, industry, and management.

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

Students with valid certifications for disabilities, specific learning disorders (SLD), or special educational needs (SEN) may refer to the services, compensatory tools, dispensatory measures, and specific aids, as well as the conditions for possible access.

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
• Elements of neural networks
• Elements of methods for structured and semi-structured data
• Methods for model evaluation
• Applications and case studies

Recommended Reading/Bibliography

All slides used during the lectures and other teaching materials will be available. In general, the notes taken during the lectures and the materials provided are sufficient for exam preparation. Further readings include:

• 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 Elements of Statistical Learning, Springer, 2009 (2nd Ed.)

Teachers and Exam Board

• ANTONIO EMANUELE CINA'
• DAVIDE ANGUITA

Exam Board

• ANTONIO EMANUELE CINA' (President)
• LUCA ONETO
• DAVIDE ANGUITA (President Substitute)
• LUCA DEMETRIO (President Substitute)

Lessons

Lessons Start

The timetable for this course is available.

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.

Assessment Methods

The oral exam will focus on the discussion of the case study.

Exam Schedule

• 09/01/2026, 09:30, GENOVA
• 22/01/2026, 09:30, GENOVA
• 13/02/2026, 09:30, GENOVA
• 04/06/2026, 09:30, GENOVA
• 01/07/2026, 09:30, GENOVA
• 20/07/2026, 08:30, GENOVA
• 14/09/2026, 09:30, GENOVA

Additional Information

Please contact the professors for further information not included in the course description.