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Visualização de Informação

Code:

MEC121021

Sigla:

VI

Level:

1

Áreas Científicas
Classificação	Área Científica
OFICIAL	Informática

Ocorrência: 2022/2023 - 2S

Ativa?	Yes
Unidade Responsável:	Departamento de Sistemas e Informática
Curso/CE Responsável:

Ciclos de Estudo/Cursos

Sigla	Nº de Estudantes	Plano de Estudos	Anos Curriculares	Créditos UCN	Créditos ECTS	Horas de Contacto	Horas Totais
MEEC	2	Plano de Estudos_2020	1	-	7,5	60	202,5

Língua de trabalho

Portuguese - Suitable for English-speaking students

Objetivos

1. To introduce students to the fundamental problems, concepts, and approaches in the design and analysis of data visualization systems.


2. To familiarize students with the stages of the visualization pipeline, including data modeling, mapping data attributes to graphical attributes, perceptual issues, existing visualization paradigms, techniques, and tools, and evaluating the effectiveness of visualizations for specific data, task, and user types.


3. To enable students to develop complex / advanced interactive information visualization systems

Resultados de aprendizagem e competências

Modo de trabalho

Presencial

Programa

1. Introduction to Information Visualization
   
   
   
   • Definition
   
   
   • Basic concepts
   
   
   • Computers vs. Humans
   
   
   • Constraints
   
   
   
   


2. Data Abstraction
   
   
   
   • Data types
   
   
   • Dataset types
   
   
   • Attribute types
   
   
   • Semantics
   
   
   
   


3. Task Abstraction
   
   
   
   • Actions
     
     
     
     • Analyze
     
     
     • Produce
     
     
     • Search
     
     
     • Query
     
     
     
     
   
   
   • Targets
   
   
   • Analyzing and Deriving
   
   
   
   


4. Analysis – Levels for Validation
   
   
   
   • Levels of Design
   
   
   • Angles of Strike
   
   
   • Validation Approaches
     
     
     
     • Domain
     
     
     • Abstraction
     
     
     • Idiom
     
     
     • Algorithm
     
     
     • Mismatches
     
     
     
     
   
   
   • Examples of Validation
   
   
   
   


5. Marks and Channels
   
   
   
   • Defining Marks and Channels
     
     
     
     • Channel Types
     
     
     • Mark Types
     
     
     
     
   
   
   • Using Marks and Channels
   
   
   • Channels Effectiveness
   
   
   
   


6. Rules of Thumb
   
   
   
   • 2D
   
   
   • 3D
   
   
   • Eyes vs Memory
   
   
   • Resolution vs Immersion
   
   
   • Overview and Detail
   
   
   • Responsiveness Capacity
   
   
   
   


7. Arrange Tables
   
   
   
   • Arrange by Keys and Values
   
   
   • Separate, Order, and Align
   
   
   • Spatial Axis Orientation
   
   
   • Spatial Layout Density
   
   
   
   


8. Arrange Spatial Data
   
   
   
   • Geometry
   
   
   • Scalar Fields
   
   
   • Vector Fields
   
   
   • Tensor Fields
   
   
   
   


9. Arrange Networks and Trees
   
   
   
   • Connection – Link Marks
   
   
   • Matrix Views
   
   
   • Hierarchy Marks
   
   
   
   


10. Map Color and Other Channels
    
    
    
    • Color Theory
    
    
    • Colormaps
    
    
    • Other Channels
    
    
    
    


11. Manipulate Views
    
    
    
    • Change View over Time
    
    
    • Select Elements
    
    
    • Changing Viewpoint
    
    
    • Reducing Attributes
    
    
    
    


12. Facet into Multiple View
    
    
    
    • Juxtapose and Coordinate Views
    
    
    • Partition into Views
    
    
    • Superimposed layers
    
    
    
    


13. Reduce Items and Attributes
    
    
    
    • Filter
    
    
    • Aggregate
    
    
    
    


14. Embed: Focus + Context
    
    
    
    • Elide
    
    
    • Superimpose
    
    
    • Distort
    
    
    • Costs and Benefits: Distortion
    
    
    
    


15. Applications: Case Studies

Bibliografia Obrigatória

Collin Ware; Information Visualization: PERCEPTION FOR DESIGN. Fourth Edition, Morgan Kaufmann, 2021
Christian Tominski, Heidrun Schumann; Interactive Visual Data Analysis, CRC Press Taylor and Francis Group, 2020
Robert Grant; Data Visualization Charts, Maps, and Interactive Graphics, Chapman and Hall/CRC, 2018
Claus O. Wilke; Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, O’Reilly, 2019

Bibliografia Complementar

Abha Belorkar; Interactive Data Visualization with Python Second Edition, Packt Publishing, 2020
Ossama Embarak; Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI Systems, APRESS, 2018
T. Munzner; Visualization Analysis & Design: Abstractions, Principles, and Methods, CRC Press, 2014

Métodos de ensino e atividades de aprendizagem

Teaching will have 3 major components:

• Theoretical-practical classes - partially expository and with intensive use of supervised resolution of exercises, analysis of study cases and two seminars on specific topics, which will take place entirely in an online way (remote learning).
• Laboratory classes - for supervised execution and individual assessment of practical work in a computing environment for personal computers, internet, and mobile devices.
• Tutorial guidance - for personalized monitoring of the execution of distance projects.

Document files will be made available with the subject of laboratory exercises to be executed autonomously (asynchronous regime), but with monitoring by videoconference at the established time and, the use of synchronous classes (by video conference) for clarification of doubts and individual monitoring.

Software

Anaconda Distribution (Linguagem de Programação Python + Módulos de Análise e Visualização de Dados)
IDE: Visual Studio Code

Tipo de avaliação

Distributed evaluation without final exam

Componentes de Avaliação

Designation	Peso (%)
Apresentação/discussão de um trabalho científico	30,00
Teste	20,00
Trabalho laboratorial	50,00
Total:	100,00

Componentes de Ocupação

Designation	Tempo (Horas)
Apresentação/discussão de um trabalho científico	2,00
Elaboração de projeto	40,00
Estudo autónomo	30,00
Frequência das aulas	60,00
Trabalho de investigação	30,00
Trabalho laboratorial	30,00
Total:	192,00

Obtenção de frequência

The evaluation will cover all 3 components, namely:

• Through two seminars for which students will have to prepare their presentations autonomously. Theoretical knowledge and the ability to apply it to specific cases will be evaluated.
• Through a selection of the best laboratories, accompanying execution skills will be evaluated.
• Through the execution of a project (individual or up to 2 students) the capacity for autonomous work and execution will be evaluated.

Fórmula de cálculo da classificação final

Final evaluation will be:

40% Project + 10% Laboratories + 15% Seminar 1 + 15% Seminar 2 + 20% Test