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Presentation
Presentation
This module covers a variety of topics related to information visualisation from a theoretical and decision-making perspective for design choices. Students learn the conceptual foundations of data visualisation that allow for revealing information not explicit in the source data.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Master Degree | Semestral | 7
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Year | Nature | Language
Year | Nature | Language
2 | Optional | Português
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Code
Code
ULHT6347-25232
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
S1. Introduction to Data Visualization: definition, approaches, and examples S2. Phases of Visual Information Processing: perception, interpretation, and comprehending S3. Principles of Visualization Design S4. Process of Visualization Design S5. Impact of Visual Perception on Visualization Design S6. Types of Charts and Their Utilization S7. Data Distortion in Visualizations
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Objectives
Objectives
LO1. Understand factors that affect the audience's understanding of the visualization LO2. Learning to decompose a visualization into its elements and to distinguish the essential and useful for understanding from the non-essential and distracting LO3. Understand how human visual perception works and its implications in the design of visualizations LO4. Learn the types of charts suited to the type of data to show LO5. Learn to use additional elements like annotations and colors to make the visualization message stand out LO6. Identify design decisions that distort data
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Teaching methodologies and assessment
Teaching methodologies and assessment
This course offers a robust blend of theory and hands-on practice, presented through lectures, case study analysis, and student projects developed throughout the semester. The lectures primarily serve to explain theoretical concepts and the foundational principles of data visualization. In addition to these explanatory lectures, students engage in independent yet supervised tutorial sessions. During project supervision, the instructor highlights the narrative elements essential for clarifying issues, solutions, and their rationales. This approach empowers students to sharpen their critical skills within the course's framework.
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References
References
Kirk, A. (2019). Data visualisation: A handbook for data driven design . Sage.
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Office Hours
Office Hours
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Mobility
Mobility
No
