AIMS AND CONTENT

LEARNING OUTCOMES

This contribution aims to provide advance skills in data management and computer programming for building algorithms suited to implement and deploy statistical methods and data analysis tools in practice. Combine quantitative skills with business and corporate knowledge to address business challenges, identify new business opportunities and support the management with data-driven strategies. Empower students with data visualisation tools, teamwork abilities, communication skills for an effective presentation and discussion of project results.

AIMS AND LEARNING OUTCOMES

The learning objectives that will be evaluated for the purpose of passing the final exam are summarized in the following scheme:

Knowledge and understanding: Knowledge of the main tools for the synthesis and presentation of data, through the acquisition of the main techniques of descriptive statistics; knowledge of probabilistic techniques for the analysis of simple random phenomena; acquisition of basic statistical inference tools for estimation, hypothesis testing and regression analysis problems.

Ability to apply knowledge and understanding: Ability to use the appropriate techniques based on the type of data under analysis; be able to carry out basic descriptive analyzes for univariate and bivariate phenomena using the main summary indices; be able to carry out simple computations in situations of uncertainty; know how to apply the main statistical inference techniques; know how to carry out dependence/independence and regression analyses, also in the inferential context; know how to read statistical analyses carried out with the methodologies presented in the course.

Making judgements: Be able to understand and comment on the results obtained from statistical analyses in practical examples based on the context of the application, thus being able to use the results in decision-making processes.

Communication skills: Acquire the basics of technical statistical language to communicate clearly and without ambiguity with both statisticians and non-statisticians.

Learning skills: Be able to correctly read the results of statistical analyses, also in contexts of greater complexity than those presented in the course.

TEACHING METHODS

Lectures 75% Video, 6 hours of i-tivity and 25% classroom lesson.  Since the training objectives concern both theoretical and applicative skills, the lessons focused on methodological aspects of statistics will be alternated with exercises in which numerical problems and examples of simple analyses on real data are addressed.

Please note that this course is delivered online (9 hours online videos + 6 hours in person) via the Learn platform. To request access, students must contact the Settore metodi e contenuti at edunext@aulaweb.unige.it.

Exam accommodations for students with disabilities or SLD Students with a valid disability certificate or a diagnosis of Specific Learning Disabilities (SLD) (under Italian Law 104/1992) or Special Educational Needs (SEN), who are registered with the University’s Inclusion Services, can request compensatory tools and/or dispensatory measures for exams.

Students with disabilities, with SLD or with SEN are reminded that, to request exam accommodations, they must first upload their certification to the University website at servizionline.unige.it<https://servizionline.unige.it/>, in the “Students” section. The documentation will be checked by the University’s Services for the Inclusion of Students with Disabilities and with SLD.

At the beginning of the course, students are advised to contact the lecturer to agree on exam arrangements which, while respecting the learning objectives of the course, take individual learning needs into account.

To request compensatory tools or dispensatory measures, students with disabilities or SLD must fill in the dedicated Webform available athttps://unige.it/disabilita-dsa, at least 7 working days before the exam.

Students with SEN may instead send their request by e-mail to the lecturer, copying the Department Representative, Prof. Elena Lagomarsino,at inclusione.economia@unige.it, and the Inclusion Office inclusione.studenti@info.unige.it.

SYLLABUS/CONTENT

Regressione Logistica

Regression Trees

Classification Trees

Hierarchical Clustering

K-means Clustering

RECOMMENDED READING/BIBLIOGRAPHY

Trevor Hastie, Robert Tibshirani, Jerome Friedman. "The Elements of Statistical Learning. Data Mining, Inference, and Prediction." Springer

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Please note that online lectures are scheduled to begin on September 28, 2026 (first semester).

The 6-hour lectures in-person will take place from November 16 to December 11, 2026 (first semester).

Further details about the lecture timetable will be announced at a later date.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The examination consists in a written assignment.

The examination regulations are published on the course's Aulaweb page.

ASSESSMENT METHODS

The questions and exercises of the written test are chosen to cover, as far as possible, all the topics of the exam program. In addition to the degree of understanding and the ability to apply knowledge, the correct use of the technical language of the discipline and the ability to read and correctly interpret statistical analyses constitute evaluation parameters.