Scientific Visualization
A.Y. 2024/2025
Learning objectives
The course has two main purposes. The first purpose is to introduce the main data visualization techniques, which, when properly used, can allow both the visual analysis of the data, in order to discover the relevant information and relationships they express, and the effective dissemination of the obtained results. For each type of chart, its main features, functionalities and (appropriate) uses will be presented. The second purpose of the course is to provide the basic concepts for the design of "information dashboards"; they are (interactive) applications allowing a (in real time) monitoring of a system, through the (interactive and real-time) visualization of a set of system performance metrics.
Expected learning outcomes
After the course the student must be able to choose the proper graph depending on the data to be analyzed or displayed. He must be capable of designing a visually informative information dashboard for system performace assessment
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
Information Visualization and Data Visualization: similarities and differences.
Visualization and Perception.
Color and Color Perception.
Plots and graphs for data visualization e for dataset comparison: main components, characteristics, usage, advantages and drawbacks.
Infographics: descriptions and main characteristics.
Visual data analysis: classic techniques and state-of-the-art techniques.
Critical Analysis of state-of-the-art works introducing visualization techniques in scientific fields.
Open Data
Graph Visualization
Neural Network Visualization
Labs: data visualization examples (based on real problems)
Visualization and Perception.
Color and Color Perception.
Plots and graphs for data visualization e for dataset comparison: main components, characteristics, usage, advantages and drawbacks.
Infographics: descriptions and main characteristics.
Visual data analysis: classic techniques and state-of-the-art techniques.
Critical Analysis of state-of-the-art works introducing visualization techniques in scientific fields.
Open Data
Graph Visualization
Neural Network Visualization
Labs: 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: https://teams.microsoft.com/l/channel/19%3aEEF1A2GIPPHUI1qunqEk10yz_YeYxJoUOhphtJARQwE1%40thread.tacv2/General?groupId=0cd181fb-5c02-44ca-bdd9-11e6bd46acfa&tenantId=13b55eef-7018-4674-a3d7-cc0db06d545c
and papers from the literature
They will be made available via the Microsoft teams channel: https://teams.microsoft.com/l/channel/19%3aEEF1A2GIPPHUI1qunqEk10yz_YeYxJoUOhphtJARQwE1%40thread.tacv2/General?groupId=0cd181fb-5c02-44ca-bdd9-11e6bd46acfa&tenantId=13b55eef-7018-4674-a3d7-cc0db06d545c
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
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Casiraghi Elena
Shifts:
Turno
Professor:
Casiraghi ElenaEducational website(s)
Professor(s)