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 activated in the A.Y. 2022/2023
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1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)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.
Mining Massive Datasets (2021/2022)
Teaching code
4S009068
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
Secondo semestre dal Mar 7, 2022 al Jun 10, 2022.
Learning outcomes
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.
Program
- Data Mining introduction
- Finding Similar Items
- Mining Data Streams
- Frequent Itemsets
- Clustering
- Recommendation Systems
- Mining Social-Network Graphs
Bibliography
Examination Methods
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. 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.
