Discipline details

Course details

Recommended Prior Knowledge

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Objectives

At the end of the semester the student must:
1- Know and know how to apply the concepts, principles and methods of IV.
2- Select and apply the best forms of visualization for each type of data and analysis required.
3- Evaluate existing IV systems, namely to critically analyze the applicability of these systems to specific types of information and application areas.
4- Develop interactive IV systems.
Teaching Methods

Theoretical-practical classes - partially expository and with intensive resource to the resolution accompanied by exercises and analysis of cases that will take place in full distance.
Laboratory Classes - for accompanied execution and individual assessment of practical work in a computer environment.
Tutorial guidance - for personalized monitoring of remote project execution.
The evaluation:
Through two seminars for which students will have to prepare their presentations independently. Theoretical knowledge and the ability to apply it to specific cases will be assessed.
Through the evaluation of 8 laboratories, the capacities of monitored execution will be evaluated.
Through the execution of an individual project, work capacity and autonomous execution will be evaluated.
The continuous evaluation will be: 50% Project + 30% Laboratories + 10% Seminar 1 + 10% Seminar 2. In the final evaluation, the project grade is replaced by the grade obtained in the exam.
Internship(s)

Não
Syllabus

1. Visualization of Information: Object, Method and Data Types. Form vs Content
2. Information Visualization pipeline and examples of development tools
3. Perception
b. Semiotics
c. The Human Eye
d. Processing Model and
e. Low Level Aspects
f. Pre-attentive processing
g. Textures and Glyphs
h. Gestalt Laws
i. Static Standards
j. Dynamic Patterns
4. Fundamentals of Information Visualization
k. Crystallization of knowledge (Cost structure and knowledge expansion)
l. InfoVis Reference Model
m. Representation
5. Interaction
n. Introduction
o. The. Data Transformations
p. Visual Mappings
q. Visual Transformations
r. Overview & Detail
s. Focus & Context
6. Data Types and Visual Structures
t. 1D
u. 2D
v. 3D
w. Multidimensional
x. byond InfoVis
7. Special Data
y. Text and Documents
z. Temporal Dependencies
8. 3D, Virtual and Augmented Reality
Examples of development tools
9. Evaluation of InfoVis systems
10. Challenges
Content Explanation

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Methodology Explanation

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Responsible Lecturer(s)

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Bibliography

Mario Döbler, Tim Großmann ; Data Visualization with Python. Second Edition, Packt Publishing, 2020
Collin Ware ; Information Visualization: PERCEPTION FOR DESIGN. Fourth Edition, Morgan Kaufmann, 2021
Ossama Embarak ; Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI Systems, APRESS, 2018. ISBN: 978-1-4842-4108-0
Robert Grant; Data Visualization Charts, Maps, and Interactive Graphics, Chapman and Hall/CRC, 2018. ISBN: 9781138553590
Claus O. Wilke; Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, O’Reilly, 2019. ISBN: 9781492031086
T. Munzner; Visualization Analysis & Design: Abstractions, Principles, and Methods , CRC Press , 2014. ISBN: 978-1-4665-0893-4