Scheda programma d'esame
SCIENTIFIC AND LARGE DATA VISUALIZATION
DANIELA GIORGI
Anno accademico2021/22
CdSINFORMATICA
Codice656AA
CFU6
PeriodoPrimo semestre
LinguaInglese

ModuliSettoreTipoOreDocente/i
SCIENTIFIC AND LARGE DATA VISUALIZATIONINF/01LEZIONI48
MASSIMILIANO CORSINI unimap
DANIELA GIORGI unimap
Obiettivi di apprendimento
Learning outcomes
Conoscenze

Throughout the class, the students will acquire knowledge in two different fields:  

  • Information visualization and infographics
  • Scientific visualization and 3D Computer Graphics.

Topics: Fundamentals of data visualization. Visual perception. Best practices in data visualization. Visualization techniques for both scientific phenomena and abstract data. Visualization for machine learning. Representation models for geometric data. Basics of 3D rendering, lighting, and texturing.    

 

Knowledge

Throughout the class, the students will acquire knowledge in two different fields: 

 

  • 3D Computer Graphics, 
  • Scientific visualization, Information visualization and infographics. 

 

3D Computer Graphics:

Representation models for geometric data. Basics of 3D rendering, lighting, and texturing.    

Visualization:

Fundamentals of data visualization. Visual perception. Best practices in data visualization. Visualization techniques for both scientific phenomena and abstract data.

Capacità

By the end of the course, the students will be able to 

  • illustrate and communicate data and results using visualization, also for complex and large datasets;
  • represent, manipulate and display 3D data;
  • use existing visualization libraries and software tools (e.g. Seaborn, Plotly, D3.js, Paraview).  
Skills

By the end of the course, the students will be able to 

  • represent, manipulate and display 3D data;
  • illustrate and communicate data and results using visualization, also for complex and large datasets;
  • use existing visualization libraries and software tools (e.g. Seaborn, D3.js).  
Prerequisiti (conoscenze iniziali)

Basic notions of Linear Algebra, Geometry, Statistics.

Basic knowledge of Python, JavaScript+HTML.

Prerequisites

Basic notions of Linear Algebra, Geometry, Statistics.

Basic knowledge of Python, JavaScript+HTML, C++.

Indicazioni metodologiche

The course will consist of both frontal lessons and hands-on labs. 

Teaching methods

The course will consist of both frontal lessons and hands-on labs. 

Programma (contenuti dell'insegnamento)

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 Computer Graphics (including 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.

Syllabus

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 Computer Graphics (including rendering, lighting, and texturing techniques).

Then, 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. 

Modalità d'esame

The exam will consist of a practical part (a project) and a theoretical part (an oral examination, after the delivery of the project).

The practical part is worth 20/30, and the theoretical part 10/30.

Ultimo aggiornamento 31/08/2021 16:54