Planeamento

Aulas Teóricas

Lecture 1 - Introduction

Aula 1

What is InfoVis

Syllabus
Why Infovis?
Examples

Lecture 2 - Crafting the Vis

Aula 2

What? Why? How?

Iterative Design
Angles of Attack
Levels of Validation

Lecture 3 - Data Abstraction

Aula 3

Data Types

Datasets
Attribute Types
Semantics
Data Processing

Lecture 4 - Task abstraction

Aula 4

Why to abstract;
Actions.

Lecture 5 - Marks and Channels

Aula 5

Visual Fundamentals;
Marks;
Channels.

Lecture 6 - Tabular Data

Aula 6

Univariate;

Position-based bivariate;

Lecture 7 - Tabular Data II

Aula 7

Multivariate

Lecture 8 - Time

Aula 8

Characteristics of Time;

Representing time: linear vs. cyclic;
Techniques: linear time;
Techniques: cyclic time;
Intervals/spans;
Other techniques.

Lecture 9 - Visualizing Geographical Information

Aula 9

Intro;

Projections;
Point-based;
Area-based;
Path-based;
Topology;
Flow.

Lecture 10 - Interaction

Aula 10

Animation;

Select and Highlight;
Reconfigure/Re-encode;
Navigate;
Post-Wimp.

Lecture 11 - Multiple Views, Focus and Context

Aula 11

Multiple Views;

Filter and Dynamic Query;
Focus and Context.

Lecture 12 - Color

Aula 12

Color vision
Color models
Color meaning
Color maps

Lecture 13 - Simplicity & Truth

Aula 13

Keeping it simple;

Keeping it legible (understandable);
Keeping it true.

Lecture 14 - Rules of Thumb

Aula 14

No (unjustified) 3D;
No unjustified 2D;
Recognition instead of recall;
Resolution over immersion;
Overview first, zoom and filter, details on demand;
Responsiveness is required;
Get it right in black and white;
Function first, form next;
Trifecta Checkup;
Self sufficiency test.

Lecture 15 - Visualizing Relationships

Aula 15

Node-Link;

Node-Link Trees;
Matrix;
Space-Filling;
Other.

Lecture 16  - Visualizing Text

Aula 16

Vector Space Model;

Single Document Visualization;
Document Collection Visualization;
Specific Domains.

Lecture 17 - Spatial Data

Aula 17

Scalar Fields;
Vector Fields.

Lecture 18 - Complexity and Evaluation

Aula 18

Dealing with high-dimensionality data;

Item aggregation;
Attribute aggregation.
Evaluating InfoVis.

Lecture 19 - Storytelling

Aula 19

Narrative Devices for Storytelling with Data:
- History;
- The Narrative Framework;
- The Design Catalogue;
- What's next?
- Visualisation and Journalism.

Invited Lecture

Aula 20

Invited Lecture "User Experience for Analytics" by Eng. Pedro Martins, Webdetails.

Lecture 20 - Applications

Aula 21

Applications of Information Visualization;

MsC Thesis Proposals' Presentation.

Aulas Laboratoriais

Enrollment

Aula 1

Enrollment in lab classes

Introduction & Examples

Aula 2

Introduction to Information Visualization Labs.

Examples.

Checkpoint I: subject proposal

Aula 3

Checkpoint I: Visualization subject proposal.

Pentaho Data Integration Tutorial

Aula 4

Pentaho Data Integration (Kettle) Tutorial.

Checkpoint II - Dataset

Aula 5

Checkpoint II - presentation of the data abstraction for use during the creation of a visualization.

Dataset

Aula 6

Feedback on data abstraction regarding Checkpoint II

Checkpoint III: Visualization sketch

Aula 7

Checkpoint III: Evaluation and feedback of a visualization sketch using the proposed dataset and data abstraction.

D3 tutorial (Part I)

Aula 8

Introduction to D3.

D3 tutorial (Part I)

D3 tutorial (Part II)

Aula 9

D3 tutorial (Part II)

Visualization feedback

Aula 10

Feedback on the development of the visualization solution.

Checkpoint IV: First version of the Visualization

Aula 11

Checkpoint IV: Presentation and evaluation of the first functional visualization.

Visualization feedback

Aula 12

Feedback on the development of the visualization.

Visualization feedback

Aula 13

Feedback on the development of the visualization.

Checkpoint V: final visualization

Aula 14

Checkpoint V: presentation and evaluation of the final visualization.