Data Visualization

Credits
6
Types
  • MEI: Elective
  • MDS: Elective
  • MIRI: Elective
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS
The aim of the course is to introduce the basic principles of data visualization, both from a more theoretical point of view and from a practical point of view. Upon completion of the course, the student should be able to perform data cleaning, visual design, and implementation using the best-known data visualization techniques.

Teachers

Person in charge

Others

Weekly hours

Theory
1.5
Problems
0.5
Laboratory
2
Guided learning
0.082
Autonomous learning
8.418

Competences

Transversal Competences

Information literacy

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

    • Basic

  • CB7 - Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
  • CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.

    • Generic Technical Competences

    Generic

  • CG2 - Identify and apply methods of data analysis, knowledge extraction and visualization for data collected in disparate formats

    • Technical Competences

    Especifics

  • CE5 - Model, design, and implement complex data systems, including data visualization
  • CE11 - Analyze and extract knowledge from unstructured information using natural language processing techniques, text and image mining

    • Objectives

    1. Introduction to Visualization
      Related competences: CT4, CG2, CE5, CE11, CB7, CB8, CB9,
      Subcompetences
      • Goals of visualization systems
      • Basic concepts
      • History of visualization
      • Data, tasks, and users
    2. Perception
      Related competences: CE5, CE11, CB8, CB9,
      Subcompetences
      • Basic concepts
      • Preattentive variables
      • Ranking of visual variables
      • Application of perception in visualization
    3. Basic data visualization techniques
      Related competences: CT4, CE5, CE11, CB8,
      Subcompetences
      • Quantities
      • Proportions
      • Distribuciones
      • Relationships
      • Other techniques
    4. Advanced visualization techniques
      Related competences: CT4, CG2, CE5, CE11, CB7, CB8, CB9,
    5. Geospatial visualization
      Related competences: CT4, CG2, CE5, CE11, CB7, CB8,
    6. Implementation of data visualization systems
      Related competences: CT4, CG2, CE5, CE11, CB9,
      Subcompetences
      • Exploratory data analysis
      • Explanatory visualizations
    7. Trees and graphs visualization
      Related competences: CT4, CG2, CE5, CE11, CB7, CB8, CB9,
    8. Time-oriented visualization
      Related competences: CT4, CG2, CE5, CE11, CB9,
    9. Text visualization
      Related competences: CT4, CG2, CE5, CE11, CB7, CB8, CB9,
    10. Multiple Views
      Related competences: CT4, CG2, CE5, CE11, CB8,
      Subcompetences
      • Multiple views
      • Coordinated views
      • Interaction
    11. Advanced visualization concepts
      Related competences: CT4, CG2, CE5, CE11, CB8, CB9,

    Contents

    1. Visualization 101
      This section will introduce the most important visualization concepts, some bad practices will be described. The history of the display will also be discussed.
    2. Data visualization idioms
      This topic will show the most basic data visualization techniques and also present some more advanced techniques for visualizing complex data, such as multi-variable visualization or geospatial visualization.
    3. Perception
      The basic operation of the visual perception system will be explained. Some important concepts such as attentional variables, the importance of color, and the most important principles of perception will also be described. It will also describe which visual variables are perceived more carefully than others.
    4. Multiple view design
      To represent highly complex information, it is very common to need multiple variables and views. This section will cover how to design complex systems using multiple views: how to organize views, separate data, and how to create linked interactions.
    5. Implementation of data visualization applications
      There are many tools and technologies developed that allow the programming of data visualization systems. There are tools that do not require any programming such as Tableau, Vega, Lyra or that provide more control over the result using programming languages and libraries such as Altair for Python, Matplotlib for R, or D3 for JavaScript. The aim of this topic is for students to be able to assess the needs of a project in order to be able to choose the right tool. In addition, it will also be essential for students to learn how to make interactive data visualization applications using a modern library, such as Altair or Vega.
    6. Visualization for specialized data
      This section will deal with data that have a specific nature, such as geospatial data, temporal data, textual data, etc.
    7. Advanced concepts
      In this section, we will deal with advanced visualization concepts, that may include areas such as the visualization of scientific data, dimensionality reduction algorithms, etc.

    Activities

    Activity Evaluation act


    Introduction to visualization and data visualization systems

    Topic development: Introduction to visualization
    • Theory: Display definition. Importance and impact. Introduction to display systems.
    • Problems: Examples of good and bad practices.
    Objectives: 1
    Contents:
    Theory
    3h
    Problems
    1h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    2h

    Visualization techniques

    Topic development: Visual representations of data. Basic visualization techniques. Advanced visualization techniques.
    • Laboratory: Design of effective visualizations. Data cleaning. Implementation of basic data visualizations.
    • Guided learning: Practical exercises for visualizing simple data sets.
    • Autonomous learning: Data cleaning exercises. Practical exercises for visualizing simple data sets.
    Objectives: 3 4
    Contents:
    Theory
    3h
    Problems
    1h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    6h

    Perception

    Topic development: perception and color. Ranking of visual variables. Concepts of perception: preattentive variables. Principles of perception. Marks and channels. Use of color and color palettes.
    • Theory: Perception and color. Ranking of visual variables. Concepts of perception: attentive variables. Principles of perception. Brands and channels. Use of color and color palettes.
    • Problems: Perception and color. Ranking of visual variables. Concepts of perception: attentive variables. Principles of perception. Brands and channels. Use of color and color palettes.
    Objectives: 2
    Contents:
    Theory
    3h
    Problems
    1h
    Laboratory
    2h
    Guided learning
    0h
    Autonomous learning
    2h

    Techniques for specialized data visualization

    This section will deal with the specific types of data: geospatial data, text, etc., which are particular because of the way the data is represented, and the techniques required for its visualization.
    Objectives: 5 9 8 7
    Contents:
    Theory
    6h
    Problems
    2h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    4h

    Multiple view design

    Development of the topic: Design of multiple views. Organization of multiple views. Coordinated views. Interaction. Exploratory data analysis.
    • Theory: Multiple view design. Organization of multiple views. Coordinated views. Interaction. Exploratory data analysis.
    • Problems: Multiple view design. Organization of multiple views. Coordinated views. Interaction. Exploratory data analysis.
    • Laboratory: Implementation of coordinated multiple view systems. Implementation of cross-interaction.
    • Guided learning: Implementation of coordinated multiple view systems. Implementation of cross-interaction.
    • Autonomous learning: Implementation of coordinated multiple view systems. Implementation of cross-interaction.
    Objectives: 10
    Contents:
    Theory
    1.5h
    Problems
    0.5h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    10h

    Advanced visualization concepts

    In this section, advanced concepts will be introduced, such as dimensionality reduction algorithms, visualization of scientific data, etc.
    • Theory: In this section, advanced concepts will be introduced, such as dimensionality reduction algorithms, visualization of scientific data, etc.
    Objectives: 11
    Contents:
    Theory
    1.5h
    Problems
    0.5h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Implementation of data visualization applications.

    Learning a data visualization tool or library. Data visualization project.
    • Laboratory: Learning a data visualization tool or library. Data visualization project.
    • Guided learning: Learning a data visualization tool or library. Data visualization project development.
    • Autonomous learning: Learning a data visualization tool or library. Data visualization project development.
    Objectives: 2 1 3 10 6 5 8 4
    Contents:
    Theory
    0h
    Problems
    0h
    Laboratory
    18h
    Guided learning
    1h
    Autonomous learning
    53h

    Lab1 delivery

    Delivery of the first part of the project: Static visualization
    Objectives: 2 1 3 6 5
    Week: 6 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Lab2 delivery

    Delivery of the second part of the project: Lab2
    Objectives: 2 1 3 10 6 5 9 8 7 4
    Week: 14 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Final exam

    There will be a final test to demonstrate the knowledge acquired in the subject.
    Objectives: 2 1 3 10 6 5 9 8 7 4 11
    Week: 15 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Mid term exam

    Mid term exam
    Objectives: 2 1 3 6 5 4
    Week: 8 (Outside class hours)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Teaching methodology

    The subject will be taught with theoretical content and problems that will be raised during the development of the theoretical classes and more technical content taught in the laboratory classes. In the laboratories, you will start by solving simple visualization exercises and then you will move on to developing a project in two stages. In a first stage, a static multi-view visualization will be performed and in a second stage, interaction and more complex visualization elements will be added.

    Evaluation methodology

    The subject will be evaluated with a project that will have two deliveries, a midterm exam (MidTerm), and a final exam (FinalExam). The first installment will be a static display (Lab1) and the second will be an interactive display (Lab2). The final grade will be: NF = 0.15 * Lab1 + 0.25 * Lab2 + 0.2 * MidTerm + 0.4 * FinalExam

    Bibliography

    Basic

    Previous capacities

    Students should have a basic knowledge of statistics and eventually computer graphics. They should also be able to program in some general programming language, preferably Python.