Data Visualization

Recommended Prerequisites

Introduction to Programming, Object-Oriented Programming.

Teaching Methods

The unit includes theoretical-practical lectures, where in the theoretical part the fundamental concepts, principles and techniques are presented and explained in detail. In the practical part, lectures play the role of strengthening the connection between theoretic knowledge and its practical application. To pursue this goal we focus on problem solving and on the analysis of case studies that require combining different theoretical concepts and that promote critical reasoning.
In practical lectures the focus is on the resolution of practical exercices and development of the pratical projects.

Learning Outcomes

The CU sees information visualization (InfoVis) as a form of cognitive augmentation that should provide means to explore, analyse and communicate data, transforming data into information, and information into knowledge.
In this context InfoVis has two main roles:
- presenting results of data science applications to an end user;
- assist data scientists by supporting development through the visualization of raw data, features, ML models, etc.
The UC focuses on computational approaches to InfoVis, presenting the theoretical foundations to build effective visualizations, providing a set of classical techniques for each type of visualization structure and promoting the application of hybrid techniques or the creation of new ones.
Main competencies to be developed are: analysis and synthesis, problem solving; critical thinking; practical application of the theoretical knowledge; research.
Secondary competences are: organizing and planning; work in teams; autonomous learning; creativity.

Work Placement(s)

No

Syllabus

1. Information Visualization Fundamentals
. Definition and purpose
. Taxonomy
. Main eras

2. Data and Task Abstraction
. Data Abstraction
. Task Abstraction
. Analysis and Validation
. Levels of Design
. Validation approaches

4. Semiology, Visual Encoding, Representation
. Semiology of the graphic sign-system
. Utilisation of the graphic sign-system

5. Design principles and patterns
. Principles
. Patterns

6. Visualisation Pipeline
. Import
. Filter
. Encode
. Render
. Validate

7. Tabular Data
. Keys and Values
. Separate, Order, Align
. Spatial Axis Orientation
. Spatial Layout Density

8. Multivariate analysis techniques
. Geometric
. Data Glyphs
. Pixel Oriented

9. Spatial Data / Georeferenced Visualization
. Thematic cartography and geovisualization
. Geometry
. Scalar fields

10. Networks and trees
. Tree layouts
. Graph Layouts
. Concept maps and mind maps

11. Interaction
. Tasks
. Manipulation
. Tranformation
. Animation

Head Lecturer(s)

Evgheni Polisciuc

Assessment Methods

Assessment
Exam: 40.0%
Project: 60.0%

Bibliography

Munzner, T.: Visualization Analysis and Design, 2014.
Cairo, A.: The Art of Insight: How Great Visualization Designers Think, John Wiley & Sons, 2023.
Tufte, E.: Seeing with Fresh Eyes: Meaning, Space, Data, Truth, Graphics Press LLC, 2020.
Healy, K. (2018). Data visualization: a practical introduction. Princeton University Press.
Schwabish, Jonathan. Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks. Columbia University Press, 2021
Few, Stephen: Now You See It, 2009
Cairo, Alberto: The Functional Art, New Riders, 2013
Tufte, E: Beautiful evidence. Graphics Press, 2006
Tufte, E: The Visual Display of Quantitative Information, 2nd ed. Connecticut:, 2007
Meirelles, I: Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information
Visualizations. Rockport Publishers, 2013