COMPUTER SCIENCE LABORATORY

1. Knowledge and understanding The student will acquire knowledge of the IT tools necessary for data management, applied to environmental and territorial contexts wherever possible. Specifically, they will be able to:

• Understand computer hardware architecture and Operating System functions for resource management.

• Know how information, including multimedia, is represented in a computer system.

• Understand the operational logic of word processing software and spreadsheets for organizing (agro-environmental) databases.

• Understand the concept of an algorithm and the basic syntax of a programming language (e.g., Python).

2. Applying knowledge and understanding The student will be able to:

• Structure texts using word processing: use styles (Heading 1, Heading 2) to generate automatic tables of contents, lists of figures, captions, and footnotes.

• Use spreadsheets to process and analyze climatic, production, or agro-environmental data (logical functions, descriptive statistics, pivot tables) in order to draft structured technical documents.

• Write simple Python scripts to automate repetitive calculations (e.g., unit conversions or environmental index calculations).

3. Making judgements The student will develop the ability to:

• Evaluate the quality and integrity of digital data before using it in complex analyses (identifying errors or outliers in time series).

• Select the most suitable IT tool to solve specific calculation or graphical representation problems.

• Critically interpret the results obtained from computer processing.

• Analyze the logic of simple computer code, identifying and correcting procedural errors (debugging).

4. Communication skills The student will be able to:

• Transform raw data into easily readable charts and tables for non-technical stakeholders (public administrators, farmers, clients).

• Present projects and results through effective multimedia tools, using appropriate technical language.

5. Learning skills The student will acquire a working method that allows them to:

• Adapt independently to software updates.

• Learn more easily how to use specialized software (such as QGIS or AutoCAD) planned for subsequent years, having consolidated the logical foundations of digital data management.

Teaching methods: Lectures, hands-on laboratory sessions, and cooperative learning.

Module 1: ICT Fundamentals (Architecture and Multimedia)

·       Computer architecture: CPU, RAM, mass storage, peripherals (Von Neumann architecture).

·       The Operating System: file management, processes, and user interface.

·       Digital representation: audio, raster, and vector images.

Module 2: Productivity Software

·       Word Processing: Management of structured documents, styles, tables of contents/indices, and bibliography.

·       Spreadsheets (Excel): Formulas, logical and statistical functions, charts, and data analysis.

Module 3: Computational Thinking and Coding

·       The concept of Algorithms and Flowcharts.

·       Introduction to Python: variables, data types, input/output, control structures (if/else, loops).

The examination consists of a single oral session. The assessment is divided into two integrated components:

1. Technical Project Discussion: Candidates must submit and present a word-processed document (Word) and a spreadsheet (Excel) based on specific tasks assigned by the instructor 5 days prior to the exam date. These projects are designed to evaluate data management skills and the practical application of the software tools covered in the course.

2. Oral Examination: Following the project discussion, the exam will proceed with a theoretical interview covering the remaining syllabus topics (Architecture, Operating Systems, Multimedia, and Coding).