The course aims to provide students with both theoretical and practical skills for the conscious and advanced use of digital tools for data processing, analysis, and presentation, in both individual and collaborative contexts. In particular, students will acquire:

• proficiency in the professional use of software for word processing, spreadsheet management, and the creation of multimedia presentations;

• the ability to design and query simple relational databases;

• introductory programming skills, with reference to programming languages and computing environments commonly used in economic and scientific fields;

• awareness of the potential and limitations of emerging technologies, with an initial exposure to the fundamental concepts of artificial intelligence.

The course is structured into the following thematic modules:

1. Advanced Tools for Individual Productivity

Professional use of major applications for document management, the creation of effective presentations, and advanced data processing using spreadsheets. Emphasis will be placed on advanced features for automation, dynamic content management, and format customization.

2. Database Management and Querying

Fundamentals of relational databases and introduction to conceptual design. Creation and querying of databases through graphical interfaces and SQL language. Use of select, update, append, and delete queries.

3. Introduction to Programming and Computational Thinking

Basics of programming with reference to modern languages (e.g., Python, MATLAB). Control structures, functions, data manipulation, basic algorithms, and semantics. Applications in economic and scientific contexts.

4. Process Automation and Development of Simple Applications

Design of macros and scripts to automate repetitive tasks within the Office environment. Introduction to object-oriented programming and application interaction. Development of custom procedures in integrated environments.

5. Integration of Digital Tools and Data Management

Linking documents, spreadsheets, and databases. Interaction among applications for data processing and exchange, including in web-based environments. Introduction to standard connectors (e.g., ODBC) for accessing external data.

6. Emerging Technologies and Artificial Intelligence

Introductory overview of the fundamental concepts of artificial intelligence and machine learning. Basic applications of AI tools for data analysis and the construction of simple predictive models.

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Knowledge and understanding

By the end of the course, students will have acquired a solid understanding of both basic and advanced digital technologies for data management and analysis, content structuring, and problem-solving using software tools and programming languages. In particular, they will understand the functioning of key application environments, data management systems, and the theoretical foundations of programming and artificial intelligence.

These objectives will be supported through lectures, hands-on exercises, and thematic seminars.

Applying knowledge and understanding

Students will be able to independently and effectively use professional applications for individual productivity, develop advanced spreadsheets, build and query databases, design basic algorithms, create macros and scripts for process automation, and understand the basics of scientific computing. They will also be introduced to programming tools (Python, MATLAB) and basic applications of artificial intelligence.

These skills will be developed through individual and group exercises and laboratory activities.

Making judgements

Students will be capable of integrating the acquired knowledge in diverse contexts, critically evaluating the most effective solutions for managing and organizing digital information, solving computational problems, or automating procedures. They will be able to reflect independently on the use of digital tools in relation to different application contexts.

This autonomy will be encouraged through guided discussions and advanced exercises.

Communication skills

Students will develop the ability to communicate clearly and effectively, using appropriate technical terminology related to the tools and programming languages covered in the course. They will be able to present content, analyses, and solutions both orally and in writing, also using digital presentation tools.

The presentation of assignments, in-class discussions, and multimedia presentations will support the achievement of this goal.

Learning skills

Students will develop autonomous learning strategies that will enable them to deepen and update their knowledge. They will be able to approach new technologies and operational environments with a critical and flexible mindset and to transfer the acquired skills to interdisciplinary or professional contexts.

These abilities will be strengthened through laboratory activities, independent exercises, and the production of individual assignments.

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

The course includes an extensive practical component to support theoretical learning. For each topic covered, practical exercises are planned to consolidate acquired skills and foster students’ operational autonomy. Activities will take place primarily in the computer lab, with direct use of the software and development environments presented during the lessons.

Additional exercises will be assigned for individual or group work, including asynchronous activities, supported by supplementary materials.

Teaching

The course employs a variety of teaching methods designed to promote active and progressive learning:

• Lectures introducing the theoretical foundations of the core concepts;

• Practical exercises involving direct use of the software tools and programming languages covered;

• Applied activities to be carried out individually or in groups, including outside regular class hours;

• Analysis and development of case studies inspired by real-world scenarios, proposed by the instructor or, upon request, by students;

• In-depth seminars on specific topics or emerging technologies.

Innovative teaching methods

The course adopts an experiential, “learning by doing” approach. In addition to theoretical lectures, each topic is accompanied by applied cases inspired by real or realistic scenarios, presented through datasets, operational settings, or concrete problems. Students are required to implement practical solutions using the software tools and programming languages introduced during the course.

Course books

The teaching material for attending students, including slides, handouts, exercises, and supplementary readings, is made available on the Moodle platform › http://blended.uniurb.it.

Assessment

The final assessment consists of a written exam with 30 multiple-choice questions, each with only one correct answer.

Evaluation criteria

The exam is designed to assess:

• mastery of the fundamental concepts covered in the course;

• the ability to apply knowledge to operational situations and practical cases;

• accuracy and appropriateness in selecting the correct answers.

Scoring system

• Each correct answer: 1 point

• Incorrect or unanswered questions: 0 points

The maximum score is 30/30. The exam is considered passed with a score of 18/30 or higher.

Students who score between 15/30 and 17/30 may take an additional oral exam to further assess their understanding and application of the course content.

Duration of the written exam: 45 minutes.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Teaching

Downloadable materials will be provided on the instructor’s webpage on the Blended Learning platform: http://blended.uniurb.it.

Course books

The teaching material for non-attending students is the same as that provided to attending students, including slides, handouts, exercises, and supplementary readings, and is available on the Moodle platform › http://blended.uniurb.it.

Assessment

The final assessment consists of a written exam with 30 multiple-choice questions, each with only one correct answer.

Evaluation criteria

The exam is designed to assess:

• mastery of the fundamental concepts covered in the course;

• the ability to apply knowledge to operational situations and practical cases;

• accuracy and appropriateness in selecting the correct answers.

Scoring system

• Each correct answer: 1 point

• Incorrect or unanswered questions: 0 points

The maximum score is 30/30. The exam is considered passed with a score of 18/30 or higher.

Students who score between 15/30 and 17/30 may take an additional oral exam to further assess their understanding and application of the course content.

Duration of the written exam: 45 minutes.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

The exam may be taken in English upon request by the student.