Data Processing Systems
Academic Year 2025/2026 - Teacher:
SALVATORE PASQUALE
Expected Learning Outcomes
At the end of the course, students will have achieved the following learning outcomes according to the Dublin descriptors.
Knowledge and understanding: knowledge and understanding of the architecture of processing systems and the structure of computer networks, Internet security and privacy, the basics of artificial intelligence, spreadsheets and presentation tools.
Applying knowledge and understanding: conscious use of computers and the network, operating systems, network privacy and security, artificial intelligence and its limitations, spreadsheets for processing meteorological, environmental and biological data, and presentation tools for representing ideas/projects/results.
Making judgements: ability to analyse IT tools for ethical and legal awareness in the management of sensitive data (privacy) and network security, recognise the potential and limitations of artificial intelligence, collect and statistically analyse meteorological, environmental and biological data using spreadsheets, present meteorological, environmental and biological data in a scientific and concise manner using presentation tools.
Communication skills: use technical vocabulary specific to environmental informatics and use IT tools for interprofessional communication.
Learning skills: use technical vocabulary specific to environmental informatics through IT tools for daily work in the environmental field.
Course Structure
Lectures for the theoretical part. Guided exercises for Excel and PowerPoint.
Required Prerequisites
Knowledge of the Windows operating system, Microsoft office automation environments, the main functions and features of the Internet, and some knowledge of mathematical functions and summations for artificial intelligence.
Attendance of Lessons
Mandatory according to degree programme/department regulations.
Detailed Course Content
Processing systems architecture: information theory, functional logic model, CPU, memory, bus, peripherals.
Operating systems and application software. Computer networks: LAN and WAN, Internet.
Artificial intelligence: history, state of the art and evolution, neural networks, machine learning, backpropagation, various applications and medical-scientific applications.
Internet security and privacy: online life, big data, privacy, cloud, social networks, commercial value of Internet users, threats and precautions, cookies, WiFi networks, passwords, legal protections, advertising, spam and hoaxes.
Excel spreadsheets: tables, operations and expressions, statistical, mathematical and search functions, graphs, conditional formatting.
PowerPoint presentations: use and development of the application, management of text, tables, graphs, organisational charts, drawings, multimedia files and external links, animations and revisions.
Textbook Information
D. P. Curtin et al. ‘Informatica di base’ Mc Graw Hill.
Slides provided by the lecturer.
Course Planning
| | Subjects | Text References |
|---|
| 1 | Computer architecture | |
| 2 | Computer networks and the Internet | |
| 3 | Internet security and privacy | |
| 4 | Artificial Intelligence | |
| 5 | Excel | |
| 6 | Powerpoint | |
Learning Assessment
Learning Assessment Procedures
The written exam, consisting of open-ended questions, is assessed based on consistency and accuracy with the topics covered during the course.
Practical exams are assessed based on the files produced by students during the tests and corrected by the teacher on a PC.
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