Scientific Visualization
A.Y. 2025/2026
Learning objectives
This course explores advanced data visualization techniques, extending beyond basic charts to multidimensional, longitudinal, and network-based representations. Emphasizing their role in visual data analysis and storytelling, the course covers theoretical principles of visual perception, advanced visualization frameworks, and techniques for large-scale data visualization. Additionally, students will examine the principles behind designing and developing information dashboards for decision-making and system monitoring.
Expected learning outcomes
By the end of this course, students will be able to apply advanced visualization techniques, including multidimensional, longitudinal, and network-based representations, to analyze complex data and develop large-scale visualizations. They will understand the principles of visual perception, and will be able to visually communicate insights through a fair data storytelling.
Lesson period: First four month period
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First four month period
Course syllabus
Information Visualization and Data Visualization: similarities and differences.
Visualization and Perception.
Color Perception and usage of Color in data visualization.
Plots and graphs for data visualization and for dataset comparison: main components, characteristics, usage, advantages and drawbacks.
Infographics: descriptions and main characteristics.
Critical Analysis of visualization techniques in scientific fields.
Introduction to Open Data
Graph Visualization and Neural Network Visualization to enhance interpretability
Lab-inspired lectures will show data visualization examples (based on real problems)
Visualization and Perception.
Color Perception and usage of Color in data visualization.
Plots and graphs for data visualization and for dataset comparison: main components, characteristics, usage, advantages and drawbacks.
Infographics: descriptions and main characteristics.
Critical Analysis of visualization techniques in scientific fields.
Introduction to Open Data
Graph Visualization and Neural Network Visualization to enhance interpretability
Lab-inspired lectures will show data visualization examples (based on real problems)
Prerequisites for admission
Basics of math, linear algebra, vector geometry (operations between vectors, scalar product, etc.).
Suggested Courses: Statistics, Matematics, Programming
Suggested Courses: Statistics, Matematics, Programming
Teaching methods
Lectures suggested frequency
Teaching Resources
ppt Slides from each lecture,
and papers from the literature
They will be made available via the Microsoft teams channel
and papers from the literature
They will be made available via the Microsoft teams channel
Assessment methods and Criteria
Students will need to prepare a group project (2-4 people) in which they will visualize a dataset of their choice. The project will be evaluated on a scale of thirty and communicated to the students via Teams; they may potentially discuss critical phases of the project.
During the evaluation of the project, the level of understanding of the topics will be assessed.
The evaluation of the project will concern the tools used to carry out the project and the relevance of the project presentation to the topics covered in class.
Grades will be: negative, 18<=grade<=30 cum laude
During the evaluation of the project, the level of understanding of the topics will be assessed.
The evaluation of the project will concern the tools used to carry out the project and the relevance of the project presentation to the topics covered in class.
Grades will be: negative, 18<=grade<=30 cum laude
Professor(s)