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
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2° Year It will be activated in the A.Y. 2025/2026
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1 module among the following2 modules among the following1 module among the following
- A.A. 2024/2025 Complex systems and social physics - Network science and econophysics - Statistical methods for business intelligence not activated
- A.A. 2025/26 Network science and econophysics not activated1 module among the following2 modules among the followingLegend | 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.
Mining Massive Datasets (2024/2025)
Teaching code
4S009068
Teacher
Coordinator
Credits
6
Also offered in courses:
- Mining Massive Datasets of the course Master's degree in Data Science
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
Semester 2 dal Mar 3, 2025 al Jun 13, 2025.
Courses Single
Authorized
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
Python programming; computational complexity basics
Program
- Data Mining introduction
- Finding Similar Items
- Mining Data Streams
- Frequent Itemsets
- Clustering
- Recommendation Systems
- Mining Social-Network Graphs
Bibliography
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.
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
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