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.
The course aims to provide students with foundational knowledge and practical skills in data visualization, grounded in principles of human vision and perception. It introduces methods and tools for the visual analysis of various types of data—spatial, non-spatial, and temporal—emphasizing clarity, effectiveness, and usability in the design of visual representations.
By the end of the course, students will be able to:
Demonstrate an understanding of the cognitive and perceptual principles that underlie effective visual communication.
Identify and apply appropriate visualization techniques based on data type and analytical goals.
Design, implement, and critique visualizations for spatial, non-spatial, and temporal datasets.
Use visualization tools to support data exploration, pattern recognition, and communication.
Evaluate the effectiveness of visual representations in terms of readability, interpretability, and audience engagement.
Students are expected to have prior knowledge of web programming (HTML, CSS, JavaScript) and basic data analysis concepts, including familiarity with data structures, statistics, and the use of programming languages such as Python or R for data processing.
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.
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, rawgraphs
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: ANNALISA BARLA: on demand, upon explicit request by email
ANNALISA BARLA (President)
CLAUDIO MANCINELLI
ENRICO PUPPO (President Substitute)
According to the calendar approved by the Degree Program Board: https://corsi.unige.it/en/corsi/10852/studenti-orario
The timetable for this course is available here: EasyAcademy
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 Depth of oral exam proportional to attendance.
The score of homework will guide the selection of the topics during the oral exam
Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/10852/studenti-disabilita-dsa
Assessment is based on two components:
Project: Students will develop a data visualization project that demonstrates their ability to apply the principles, methods, and tools covered in the course. Particular attention will be given to the quality of data analysis, the statistical soundness of the insights, and the effectiveness of the visual representation. Projects will be evaluated on analytical rigor, clarity of communication, technical execution, and design quality.
Oral Discussion: An individual oral examination will assess the student’s understanding of theoretical concepts, their ability to justify analytical and design choices made in the project, and their critical reflection on the entire visualization process, including data preparation and interpretation.
Both components must be passed to successfully complete the course.
For further information, please refer to the course’s AulaWeb module or contact the instructor.
