Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

1° Year

ModulesCreditsTAFSSD

2° Year  activated in the A.Y. 2023/2024

ModulesCreditsTAFSSD
activated in the A.Y. 2023/2024
ModulesCreditsTAFSSD
Modules Credits TAF SSD
Between the years: 1°- 2°
1 module among the following (a.a. 2023/24: Data protection in business organizations not activated)
6
C
IUS/17
Between the years: 1°- 2°
2 modules among the following (a.a. 2023/24: Statistical methods for business intelligence not activated)
Between the years: 1°- 2°
2 modules among the following (a.a. 2023/24: Complex systems and social physics not activated)
Between the years: 1°- 2°
2 modules among the following
Between the years: 1°- 2°

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S009068

Coordinator

Damiano Carra

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Period

Semester 2 dal Mar 6, 2023 al Jun 16, 2023.

Learning objectives

The course aims to present the main algorithmic solutions for the analysis and extraction of information from large amounts of data. Particular emphasis is given to distributed approaches and parallel algorithms. At the end of the course the student has to show to have acquired the following skills:
- the knowledge necessary for the design of algorithms for the analysis of unstructured data and the interpretation of the results
- ability to develop cost/benefit analysis of the developed data analysis models
- ability to compare different data analysis techniques, choosing the most suitable among them according to the available computing resources and to design innovative solutions appropriately
- acquisition of the basis for continuing your studies independently in the context of developing advanced analyzes of large amounts of data.

Prerequisites and basic notions

To follow the course it is recommended to have knowledge of programming in Python

Program

- Data Mining introduction
- Finding Similar Items
- Mining Data Streams
- Frequent Itemsets
- Clustering
- Recommendation Systems
- Mining Social-Network Graphs

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

Lectures in the classroom

Learning assessment procedures

Examination consists of a project and the corresponding documentation. The project aims at verifying the comprehension of course contents and the capability to apply these contents in the resolution of a problem.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

The project topic is agreed with the teacher and focus on specific case studies. The project includes the performance evaluation for different input sizes, and the evaluation of the implementation alternatives. After the evaluation of the project documentation, the student may give an oral exam where the details of the project are discussed.

Criteria for the composition of the final grade

The grade is based on the evaluation of both the material provided and the student's autonomy

Exam language

English