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.
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.
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.
By the end of the course, students will be able to:
Explain the basic mechanisms of visual perception and apply them to the design of effective visualizations.
Critically select the most appropriate visualization techniques depending on the type and structure of the data.
Design and implement visualizations for spatial, non-spatial, and temporal data using appropriate digital tools.
Evaluate the communicative effectiveness of a visualization, identifying potential issues such as ambiguity, cognitive overload, or inefficiency.
Apply the acquired principles to produce visual analyses aimed at synthesizing, communicating, and exploring data in real-world contexts.
Basics of web development (HTML, CSS, JavaScript) Basic notions of statistics
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.
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
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
Ricevimento: On demand, upon explicit request by email
ANNALISA BARLA (President)
CLAUDIO MANCINELLI
ENRICO PUPPO (President Substitute)
In agreement with the calendar approved by the Degree Program Board of Computer Science.
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
