Information Visualization

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Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS
Mail
El objetivo del curso de visualización de la información es dar a los alumnos una serie de principios para elaborar aplicaciones de visualización de datos y guiarlos en el aprendizaje de las herramientas que se necesitan para realizar aplicaciones de visualización de forma eficiente y eficaz.
Los contenidos incluirán los fundamentos teóricos de la visualización, teoría de la percepción, el pipeline de visualización, los diferentes tipos de representación de la información y los métodos principales de interacción.

Teachers

Person in charge

  • Pere Pau Vázquez Alcocer ( )

Weekly hours

Theory
1.5
Problems
0.5
Laboratory
2
Guided learning
0.4
Autonomous learning
4

Competences

Technical Competences

Technical competencies

  • CE1 - Skillfully use mathematical concepts and methods that underlie the problems of science and data engineering.
  • CE4 - Use current computer systems, including high performance systems, for the process of large volumes of data from the knowledge of its structure, operation and particularities.
  • CE5 - Design and apply techniques of signal processing, choosing between different technological tools, including those of Artificial vision, speech recognition and multimedia data processing.
  • CE7 - Demonstrate knowledge and ability to apply the necessary tools for the storage, processing and access to data.
  • CE10 - Visualization of information to facilitate the exploration and analysis of data, including the choice of adequate representation of these and the use of dimensionality reduction techniques.

Transversal Competences

Transversals

  • CT3 - Efficient oral and written communication. Communicate in an oral and written way with other people about the results of learning, thinking and decision making; Participate in debates on topics of the specialty itself.
  • CT4 - Teamwork. Be able to work as a member of an interdisciplinary team, either as a member or conducting management tasks, with the aim of contributing to develop projects with pragmatism and a sense of responsibility, taking commitments taking into account available resources.
  • CT7 - Third language. Know a third language, preferably English, with an adequate oral and written level and in line with the needs of graduates.

Basic

  • CB3 - That students have the ability to gather and interpret relevant data (usually within their area of ??study) to make judgments that include a reflection on relevant social, scientific or ethical issues.
  • CB4 - That the students can transmit information, ideas, problems and solutions to a specialized and non-specialized public.

Generic Technical Competences

Generic

  • CG2 - Choose and apply the most appropriate methods and techniques to a problem defined by data that represents a challenge for its volume, speed, variety or heterogeneity, including computer, mathematical, statistical and signal processing methods.

Objectives

  1. Introduction to Information Visualization
    Related competences: CE4, CE10, CT3, CG2, CB3, CB4,
    Subcompetences:
    • The Visualization Mantra
    • Basics
    • Historia
    • The information visualization process
  2. Introducción a la percepción visual
    Related competences: CE10, CT3, CG2,
    Subcompetences:
    • Fundamentals of human perception
    • Marks and channels
    • Color and perception
  3. Design of information visualization systems
    Related competences: CE7, CE10, CT3, CG2, CB3, CB4,
    Subcompetences:
    • Basic principles of visualization
    • Elements of an information visualization system
    • Visualization design
  4. Exploratory data analysis
    Related competences: CE5, CE10, CT3, CG2, CB4,
    Subcompetences:
    • Data wrangling
    • Data presentation
    • Hypothesis testing
  5. Visualization of multi-dimensional data
    Related competences: CE1, CE4, CE5, CE7, CE10, CT3, CT4, CT7, CG2, CB3, CB4,
    Subcompetences:
    • Multiple marks and channels
    • Complex diagrams: Trellis, SPLOM, PCP
    • Views
  6. Interaction and animation
    Related competences: CE5, CE7, CE10, CT3, CT4, CT7, CG2, CB4,
    Subcompetences:
    • Navigation
    • Selection and pointing
    • Filtering
  7. Multiple views and coordinated views
    Related competences: CT3, CB3, CB4,
    Subcompetences:
    • Effective use of space
    • Overlapping and juxtaposition
  8. Focus and context
    Related competences: CE1, CE10, CT3, CT4, CG2, CB3, CB4,
    Subcompetences:
    • Eliding information
    • Information overlapping
    • Distortion
  9. Item and attributes reduction
    Related competences: CE1, CE5, CE10, CG2, CB4,
  10. Validation of visualization systems
    Related competences: CE1, CE10, CT3, CB4,
    Subcompetences:
    • Domain validation
    • Validation of abstraction
    • Validation of the representation
    • Validation of the algorithm
  11. Implementation of visualization applications
    Related competences: CE1, CE4, CE5, CE7, CE10, CT3, CT4, CT7, CG2, CB3, CB4,
    Subcompetences:
    • Visualization coding
    • Data processing
    • Design of coordinated views
  12. Advanced visualization tècniques
    Related competences: CE4, CE7, CT3, CG2, CB3,

Contents

  1. Introduction to visualization
    In this topic we will discuss the need for visualization of data and the objectives of the visualization tools.
  2. Perception and color
    Visual perception is a very important factor when creating visualizations, since the visual system is the one that receives the greatest amount of information that we perceive. In this topic we will talk about the visual system, and some theories of the perception of color and forms.
  3. Visual representations of the data
    There are a large number of methods of data representation: tables, graphs, trees, etc. In this topic we will visit them and we will end up giving some guides to select the most appropriate representation for each problem.
  4. Visualization of multiple data
    In many cases, the information that we want to represent will be highly complex and we will often find ourselves in the situation of having to represent multiple variables. Here we will discuss different possibilities that will be detailed in later issues.
  5. Animation and interaction
    To explore the data, you must be able to work on visual representations. This topic will see data changes in different dimensions: time, point of view ...
  6. View manipulation
    To explore the data, you must be able to work on visual representations. In this section you will see changes of data in different dimensions: time, point of view ...
  7. Advanced data representation systems
    Advanced data representation systems

    - Maps

    - Time display

    - Visualization of 3D data

    - Other scientific data
  8. Implementation of information visualization applications
    There are many tools and technologies developed recently that make creating views easier, such as Tableau, Vega, Lyra or using programming languages and libraries such as D3 for JavaScript or Bokeh for Python. The objective of this subject is that students are able to perform visualization applications using some of the most modern tools.

Activities

Activity Evaluation act


Introduction to data visualization systems

Development of the theme: Introduction to visualization
Objectives: 3 1
Contents:
Theory
1.5h
Problems
0.5h
Laboratory
2h
Guided learning
0h
Autonomous learning
1h

Color and perception

Development of the subject: perception and color Ranking of Mackinlay Pre-attentive care Type of dimensions Principles of perception Brands and channels Color
Objectives: 2 3 4
Contents:
Theory
3h
Problems
1h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Design of information visualization systems

Development of topic 3: Design of information visualization systems
Objectives: 3 4 5
Contents:
Theory
2.5h
Problems
1.5h
Laboratory
0h
Guided learning
0h
Autonomous learning
3h

Exploratory data analysis

Development of the subject: Exploratory data analysis
Objectives: 3 5 6
Contents:
Theory
1h
Problems
0.5h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Exploratory data analysis

Development of the theme: Multi-dimensional view Multiple brands and channels Complex diagrams: Trellis, SPLOM, PCP Views
Objectives: 2 3 4 5 6
Contents:
Theory
1.5h
Problems
0.5h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Design of views in a commercial tool such as QlikView

Design of views in a commercial tool such as QlikView
Objectives: 4 5 11
Contents:
Theory
0h
Problems
0h
Laboratory
6h
Guided learning
2h
Autonomous learning
6h

Interaction and animation

Development of the theme: Interaction and animation
Objectives: 3 4 7 8
Contents:
Theory
3h
Problems
1h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

View manipulation

Development of the theme: View manipulation
Objectives: 5 6 7 8
Contents:
Theory
1.5h
Problems
0.5h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Focus + context

Techniques of focus and context of the data: - Delete information - Superimposition of information - Distortion
Objectives: 2 3 7 8
Contents:
Theory
1.5h
Problems
0.5h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Data reduction

Development of the subject: Data reduction
Objectives: 3 5 8 10 9
Contents:
Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
2h

Advanced data representation systems

Advanced data representation systems - Maps - Time display - Visualization of 3D data - Other scientific data
Objectives: 3 4 5 12
Contents:
Theory
3h
Problems
1h
Laboratory
0h
Guided learning
0h
Autonomous learning
3h

Validation of information visualization systems

Evaluation and validation of data visualization systems
Objectives: 3 4 10
Contents:
Theory
1.5h
Problems
0.5h
Laboratory
0h
Guided learning
0h
Autonomous learning
1h

Partial exam

Partial exam
Objectives: 2 3 4 5 6 7
Week: 7
Type: theory exam
Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Lab project

Lab project
Objectives: 3 4 11
Week: 8
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
2h
Autonomous learning
4h

Implementation of information visualization applications

Implementation of information visualization applications
Objectives: 3 4 5 11
Contents:
Theory
0h
Problems
0h
Laboratory
22h
Guided learning
2h
Autonomous learning
20h

Final exam

Final exam
Objectives: 2 3 4 5 6 7 8 10 9
Week: 15 (Outside class hours)
Type: final exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Teaching methodology

Classes will be given with the support of slides and articles.
During the classes, exercises will be proposed and resolved.

For the laboratory part, directed practices will be developed in the laboratory hours.

There will be a partial delivery of laboratory and a final project.

Evaluation methodology

During the course there will be two laboratory practices (Labo1 and Labo2). In addition, there will be a partial exam (Partial) and a final exam (Final).

The final grade is calculated as:

Final Note = 0.15 Labo1 + 0.3 Labo2 + max(0.15 Partial + .4 Final, 0.55 Final)

The re-evaluation exam substitutes the theoretical contents, not the lab part.

Bibliography

Basic:

Previous capacities

Els estudiants han de saber programar.

Addendum

Contents

No hi ha modificacions.

Teaching methodology

No hi ha modificacions.

Evaluation methodology

No hi ha modificacions.

Contingency plan

Si és necessari, les classes passaran a ser a través de videoconferència. Serà responsabilitat dels alumnes assistir a les sessions. S'intentarà mantenir els exàmens de forma presencial. Si això no es pot fer, s'habilitaran mitjans per a realitzar-los amb les millors garanties possibles, que podrien incloure fer-los de forma oral, a través d'Atenea, o substituir-los per una pràctica individual.