ECTS - University of Pisa
Throughout the class, the students will acquire knowledge in three different fields:
Topics: Fundamentals of data visualization. Visual perception. Best practices in data visualization. Visualization techniques for both scientific phenomena. 3D data visualization. Representation models for geometric data. Basics of 3D rendering, lighting, and texturing.
Throughout the class, the students will acquire knowledge in three different fields:
Topics: Fundamentals of data visualization. Visual perception. Best practices in data visualization. Visualization techniques for both scientific phenomena. 3D data visualization. Representation models for geometric data. Basics of 3D rendering, lighting, and texturing.
By the end of the course, the students will be able to
By the end of the course, the students will be able to
Basic notions of Linear Algebra, Geometry, Statistics.
Basic knowledge of Python, JavaScript+HTML, (optional) C++.
Basic notions of Linear Algebra, Geometry, Statistics.
Basic knowledge of Python, JavaScript+HTML, (optional) C++.
The course will consist of both frontal lessons and hands-on labs.
The course will consist of both frontal lessons and hands-on labs.
The availability of data has been growing and growing in recent years, generated from sensors, mobiles, and so on. Visualization is what one needs to put data to good use: it allows one to analyse, explore and communicate possibly large and complex data in a meaningful way.
The course will introduce the fundamentals of information visualization. Information visualization often deals with abstract data, which do not have an obvious visual representation: think of the network connections of a social network. We will learn to decide what to visualize, how to abstract and encode data using different charts and graph types, and how to evaluate different solutions according to perception rules.
Then, we will learn how to visualize data in low-dimensional spaces, mainly 2D and 3D. We will discuss the fundamentals of 3D representation, rendering, lighting, and texturing techniques.
Finally, we will talk about scientific visualization, which concerns the graphical illustration of scientific data (for example, biological data), with the purpose of understanding and gleaning insights on the underlying phenomena.
The availability of data has been growing and growing in recent years, generated from sensors, mobiles, and so on. Visualization is what one needs to put data to good use: it allows one to analyse, explore and communicate possibly large and complex data in a meaningful way.
The first part of the course will deal with data in low-dimensional spaces, mainly 2D and 3D: we will discuss discrete models for 3D data representation (with an emphasis on polygonal meshes), and the fundamentals of 3D representation, rendering, lighting, and texturing techniques).
Finally, we will talk about scientific visualization, which concerns the graphical illustration of scientific data (for example, biological data), with the purpose of understanding and gleaning insights on the underlying phenomena.
Finally, the last part of the course will introduce the fundamentals of information visualization. Differently from scientific visualization, where data have an immediate physical representation, information visualization often deals with abstract data, which do not have an obvious visual representation: think of the network connections of a social network. We will learn to decide what to visualize, how to abstract and encode data using different charts and graph types and how to evaluate different solutions according to perception rules.
The exam will consist of a practical part (a project) and a theoretical part (an oral examination, after the delivery of the project).
The exam will consist of a practical part (a project) and a theoretical part (an oral examination, after the delivery of the project).
Teams Class Code: i56gc6m
Teams Class Code: i56gc6m
