CODE 90529 ACADEMIC YEAR 2025/2026 CREDITS 6 cfu anno 2 COMPUTER SCIENCE 10852 (LM-18) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR INF/01 LANGUAGE English TEACHING LOCATION GENOVA SEMESTER 1° Semester TEACHING MATERIALS AULAWEB 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 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: 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. 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. 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 ANNALISA BARLA Ricevimento: ANNALISA BARLA: on demand, upon explicit request by email 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 Depth of oral exam proportional to attendance. The score of homework will guide the selection of the topics during the oral exam 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.