Information Visualization

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS
Mail
pere.pau.vazquez@upc.edu
The objective of the course of visualization of the information is to give the students a series of principles to elaborate applications of visualization of data and to guide them in the learning of the tools that are needed to realize applications of visualization of efficient way and effective
The contents will include the theoretical foundations of visualization, perception theory, visualization pipeline, different types of representation of information and the main methods of interaction.

Teachers

Person in charge

Others

Weekly hours

Theory
1.5
Problems
0.5
Laboratory
2
Guided learning
0
Autonomous learning
6

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 [Avaluable] - 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.
  • CT5 [Avaluable] - Solvent use of information resources. Manage the acquisition, structuring, analysis and visualization of data and information in the field of specialty and critically evaluate the results of such management.
  • 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: CB3, CB4, CT3, CE10, CE4, CG2,
      Subcompetences
      • The Visualization Mantra
      • Basics
      • Historia
      • The information visualization process
    2. Introducción a la percepción visual
      Related competences: CE10, CG2, CT3,
      Subcompetences
      • Fundamentals of human perception
      • Marks and channels
      • Color and perception
    3. Exploratory data analysis
      Related competences: CB4, CT3, CE10, CE5, CG2,
      Subcompetences
      • Data wrangling
      • Data presentation
      • Hypothesis testing
    4. Design of information visualization systems
      Related competences: CE7, CG2, CB3, CB4, CT3, CT5, CE10,
      Subcompetences
      • Basic principles of visualization
      • Elements of an information visualization system
      • Visualization design
    5. Focus and context
      Related competences: CB3, CB4, CT3, CT4, CE1, CE10, CG2,
      Subcompetences
      • Eliding information
      • Information overlapping
      • Distortion
    6. Interaction and animation
      Related competences: CB4, CT3, CT4, CT7, CE10, CE5, CE7, CG2,
      Subcompetences
      • Navigation
      • Selection and pointing
      • Filtering
    7. Visualization of multi-dimensional data
      Related competences: CB3, CB4, CT3, CT4, CT7, CE1, CE10, CE4, CE5, CE7, CG2,
      Subcompetences
      • Multiple marks and channels
      • Complex diagrams: Trellis, SPLOM, PCP
      • Views
    8. Multiple views and coordinated views
      Related competences: CB3, CB4, CT3,
      Subcompetences
      • Effective use of space
      • Overlapping and juxtaposition
    9. Item and attributes reduction
      Related competences: CB4, CE1, CE10, CE5, CG2,
    10. Validation of visualization systems
      Related competences: CB4, CT3, CE1, CE10,
      Subcompetences
      • Domain validation
      • Validation of abstraction
      • Validation of the representation
      • Validation of the algorithm
    11. Implementation of visualization applications
      Related competences: CB3, CB4, CT3, CT4, CT5, CT7, CE1, CE10, CE4, CE5, CE7, CG2,
      Subcompetences
      • Visualization coding
      • Data processing
      • Design of coordinated views
    12. Advanced visualization tècniques
      Related competences: CB3, CT3, CE4, CE7, CG2,

    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: 4 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 4 3
    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: 4 3 7
    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: 4 7 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 4 3 7 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: 3 7 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: 4 3 8 5
    Contents:
    Theory
    3h
    Problems
    1h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    2h

    View manipulation

    Development of the theme: View manipulation
    Objectives: 7 6 8 5
    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 4 8 5
    Contents:
    Theory
    1.5h
    Problems
    0.5h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    2h

    Data reduction

    Development of the subject: Data reduction
    Objectives: 4 7 5 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: 4 3 7 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: 4 3 10
    Contents:
    Theory
    1.5h
    Problems
    0.5h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    1h

    Partial exam

    Partial exam
    Objectives: 2 4 3 7 6 8
    Week: 7
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Lab project

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

    Implementation of information visualization applications

    Implementation of information visualization applications
    Objectives: 4 3 7 11
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    22h
    Guided learning
    2h
    Autonomous learning
    20h

    Final exam

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

    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

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