OVERVIEW

This course provides an introduction to information visualization. Students will learn the principles to design a visualization applicaiton, and they will experience advanced programming tools to develop such applications in practice. The course consists of both theoretical lectures in class and practical experiences both in class and through autonomous work of students.

AIMS AND CONTENT

LEARNING OUTCOMES

Learning basic principles from vision and human perception. Learning principles, methods, and techniques for effective visual analysis of data, including techniques for visualizing spatial, non-spatial, and temporal data.

AIMS AND LEARNING OUTCOMES

Detailed Learning Objectives

• Introduce the fundamental principles of visual perception and human vision to understand how people interpret graphical representations.

• Provide an overview of the main approaches and methods for data visualization, with attention to selecting the appropriate visualization type based on the nature of the data.

• Explore techniques and tools for representing spatial, non-spatial, and temporal data.

• Develop skills in the visual analysis of complex datasets, focusing on communicative effectiveness and usability of graphical representations.

Expected Learning Outcomes

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

1. Explain the basic mechanisms of visual perception and apply them to the design of effective visualizations.

2. Critically select the most appropriate visualization techniques depending on the type and structure of the data.

3. Design and implement visualizations for spatial, non-spatial, and temporal data using appropriate digital tools.

4. Evaluate the communicative effectiveness of a visualization, identifying potential issues such as ambiguity, cognitive overload, or inefficiency.

5. Apply the acquired principles to produce visual analyses aimed at synthesizing, communicating, and exploring data in real-world contexts.

PREREQUISITES

Basics of web development (HTML, CSS, JavaScript)
Basic notions of statistics 

TEACHING METHODS

This course uses the method of flipped classroom: students are expected to read course material before it is presented in class. 
Class lectures are for intrudicing theory and design principles. 
Practice consists in simple data visualization tasks implemented individually by students
Homework will be assigned. 
Class attendance may affect the final assessment.

SYLLABUS/CONTENT

This course will make use of elementary client-side web programming; students are expected to have some backgound on HTML5, CSS, and Javascript. 

Visual perception

Data abstraction

Marks and channels

Task abstraction

Visualization of categorical data

Visualization of temporal data

        Visualization of correlations

Visualization of geographic data

Technical tools: D3.j

RECOMMENDED READING/BIBLIOGRAPHY

Scott Murray. Interactive Data Visualization for the Web. O’Reilly, 2013 

Jonathan Schwabish. Better Data Visualizations. Columbia University Press, 2021

Koponen, Juuso, and Jonatan Hildén. Data visualization handbook. Aalto korkeakoulusäätiö, 2019.

Tamara Munzner.VisualizationAnalysis and Design.AK PetersVisualization Series. CRC Press, 2014 

Amelia Wattemberger. Fullstack D3 and Data Visualization: Build beautiful data visualizations with D3

TEACHERS AND EXAM BOARD

Exam Board

ANNALISA BARLA (President)

CLAUDIO MANCINELLI

ENRICO PUPPO (President Substitute)

LESSONS

LESSONS START

In agreement with the calendar approved by the Degree Program Board of Computer Science.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Attendance
Quiz during class - Class attendance

Homework [20%]
About four assigned along the course - small effort, strict deadlines

Project [50%]
Assigned during the course - big work, completed by the end of the course

Oral [30%]
After submitting the project
By appointment for groups of students
Depth of oral exam proportional to attendance.

The score of quizzes+homework will guide the selection of the topics during the oral exam

Exam schedule