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
| Modules | Credits | TAF | SSD |
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Mathematical analysis
2° Year It will be activated in the A.Y. 2026/2027
| Modules | Credits | TAF | SSD |
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3° Year It will be activated in the A.Y. 2027/2028
| Modules | Credits | TAF | SSD |
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One module to be chosen among the following| Modules | Credits | TAF | SSD |
|---|
Mathematical analysis
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
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One module to be chosen among the following| Modules | Credits | TAF | SSD |
|---|
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.
Applied software engineering (It will be activated in the A.Y. 2027/2028)
Teaching code
4S012390
Credits
12
Scientific Disciplinary Sector (SSD)
-
Learning objectives
Software engineering module: the course offers the theoretical and technical foundations to manage the problems associated with the management of medium-large software projects. The course aims to present the general techniques that can be used to successfully deal with the development of complex software.
The main knowledge acquired is related to: - UML - project management - design pattern basics. The main skills acquired (ability to apply the knowledge acquired) are: ability to design, develop and test software systems
Advanced programming module: The aim of the course is to provide students with the knowledge and skills necessary to design, implement and manage real-time data analysis solutions.
The course will focus on advanced technologies, such as tools for automating application deployment (docker) and process orchestration (kubernetes), "on the edge" technologies for the analysis of data ingestion in real time, data pipeline techniques , data processing and analysis tools and data visualization.
At the end of the course, students will be able to:
Understand the main methodologies and best practices in real-time data analysis
Apply these methodologies and best practices to solve real problems
Critically judge the proposed solutions
Communicate your ideas effectively in the technical field
Learn independently and continuously
The course will be structured to involve students in finding solutions to real problems.
