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

The teaching unit is intended to introduce 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

Aims

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.

Learning Outcomes

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

1. Demonstrate an understanding of the cognitive and perceptual principles that underlie effective visual communication.

2. Identify and apply appropriate visualization techniques based on data type and analytical goals.

3. Design, implement, and critique visualizations for spatial, non-spatial, and temporal datasets.

4. Use visualization tools to support data exploration, pattern recognition, and communication.

5. Evaluate the effectiveness of visual representations in terms of readability, interpretability, and audience engagement.

PREREQUISITES

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.

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.

Students who hold valid certificates relating to Specific Learning Difficulties (SLD), disabilities or other educational needs are invited to contact the lecturer and the school’s disability liaison officer at the start of the course to agree on any teaching arrangements which, whilst respecting the course objectives, take into account individual learning styles. 

The contact details for the university’s disability liaison officer are available at the following link: https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita.

SYLLABUS/CONTENT

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

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

LESSONS

LESSONS START

https://easyacademy.unige.it/portalestudenti/index.php?view=easycourse&_lang=it&include=corso

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
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 METHODS

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.

FURTHER INFORMATION

For further information, please refer to the course’s AulaWeb module or contact the instructor.

Students with valid certifications for Specific Learning Disorders (SLD) may request accommodations for exams at least 7 days prior to the exam date by filling out the “accommodation request form” (available via online services at https://modulionline.unige.it/richiesta-adattamenti# no-back), which will be automatically forwarded by the system to the instructor in charge of the course and to the faculty liaison for students with disabilities and SLDs in their School/Department. 

The student will receive a copy of their request.