Data Visualization

Recommended Prerequisites

Introduction to Programming, Object-Oriented Programming

Teaching Methods

The unit includes theoretical lectures where the fundamental concepts, principles and techniques and there are presented and explained in detail.
Lectures of practical nature 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.

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.
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