Academic Year 2023-2024



Laura Bortoloni
Unit Credits
Teaching Period
Second Period
Course Type
Prerequisites. The course requires knowledge, even at a basic level, of vector drawing software, digital image processing and layout software. Knowledge of animation and programming software is welcome, but not essential.
Teaching Methods. The lessons will be, in a first phase, theoretical and framing to create a critical and updated vision of contemporary visual cultures. In a second phase, during the laboratory lessons, students will have the opportunity to develop the notions learned theoretically, under the supervision of the teacher, through one or more practical projects. During the course there may be exercises, project analyzes and case studies useful for the development of specific skills.

During the course there will be workshops useful to review and discuss the project that the student will carry out for the exam; these reviews are essential for taking the exam.

Lessons will not be recorded. Teams will be adopted for assignments.

Verification of Learning. The exam consists in the oral presentation and discussion of a graphic project developed by the student. For the purposes of the assessment, it will be important to demonstrate a thorough understanding of the concepts imparted during the course and the ability to apply this knowledge also in an original and creative way. The project development methodology will also be important. To participate in the exam it will be essential to have carried out at least one revision of the project.
More Information. In support of teaching, slides (power-point / keynote), videos, texts and examples of project case studies and project references will be mainly used.
The aim of the course is to enable students to design visual representations for complex information systems, understanding how the generation of visual systems dialogues with the languages of communication and information, representing complex information systems through visual languages.

The aim of the course is to give students the ability to represent complex data systems through techniques and visual languages. The course aims to introduce students to the understanding of design methods for the treatment, organization and representation of information within the graphics systems for paper and screen.

Information Design and Data visualization have simplified the relationship between users and data, they have innovated as we learn new information, they have broadened the audience of users, making visible what was invisible or difficult to understand.

The course intends to teach students the techniques used to graphically represent complex information, presenting methodologies and tools to create visual elements, in heterogeneous areas, from the construction of visual identity systems, information design for exhibit, wayfinding systems, infographics and data visualization.

The course is divided into two parts: a theoretical part in which research systems, analysis and data representation will be studied and a laboratory / practical part in which these principles will be applied.

Course Outcomes:

Knowledge and understanding:

– know how to correctly use the tools of graphic design and their relevance in crafting of visual languages.

– understand the basic concepts of graphical representation of complex information a

– know how to research, acquire and analyze contemporary data systems and visual systems

– know the main data representation techniques and be able to generate static or interactive data visualizations and infographics.

Applying knowledge and understanding

– be able to design a coherent information system, from the conception of the information structure to its visual components.

– be able to visualize data

– be able to identify the correct methodology to represent the chosen dataset.

Soft skills:

Making judgements

– Be able to chose independently the right tools and techniques to realize a chosen project.


– Be able to illustrate orally and in writing technical issues related to visual representation

– Be able to visually represent a complex data system

– understand the complexities related to the user experience

Lifelong learning skills

– Knowing how to deal with the study of complex visual languages

– Knowing how to independently find and use bibliographic and IT tools useful for personal study.

– know ho to use of visual variables to represent data effectively and consistently

The course gives advanced skills in the graphic-visual representation of complex systems. The aim of the course is to provide students with a working methodology applicable in various contexts and implementable with the multiple tools made available by technology and multimedia.


1. Presenation of the course. Contemporary visual languages and variabile visual systems

2. Information design: a briwf history and ad overview on the contemporary scene

3. “Sinsemia”. Cohexintence and interaction among different visual languages

4. Graphic elements for design systems: colors, pictograms, images.

5. Information design systems and their applications on different media

6. Ehitcs and practice around data visualization: data, capta, visual variables, encoding to decode

7. A data visualization workflow: Excel, RAWgraphs

[1] Perondi, L. (2012), Sinsemie – Scrittura nello spazio, Stampa Alternativa.

[2] Cairo, A. (2013), L’arte funzionale. Infografica e visualizzazione delle informazioni, Pearson.

[3] Meirelles, I. (2013), Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations, Rockport Pub.

[4] Harris, R. L. (1999), Information Graphics. A Comprehensive Illustrated Reference, New York, Oxford, Oxford University Press.

[5] Tufte, E. R. (1983), The visual display of quantitative information, Cheshire, Connecticut, Graphic Press.

[6] McCandless , (2011), Information Is Beautiful: The Information Atlas, Harper Collins.

[7] Bertin, J. (2011). Semiology of Graphics: Diagrams, Networks, Maps. ESRI Press.

[8] Correll, M. (2019) ‘Ethical Dimensions of Visualization Research’, pp. 1–13. doi: 10.1145/3290605.3300418.

[9] Manovich, L. (2018) ‘Can We Think Without Categories?’, Digital Culture & Society, 4(1), pp. 17–28. doi: 10.14361/dcs-2018-040102.