DATA VISUALIZATION
- Overview
- Assessment methods
- Learning objectives
- Contents
- Delivery method
- Teaching methods
- Contacts/Info
Students should have acquired a background knowledge on data preparation and statistics.
Programming skills in Python or Javascript may improve the chance to acquire a deeper knowledge of tools usage.
No other pre-requisites are required.
The exam will consist of a theory part (half of the final vote) and a practical part (half of the final vote). In particular, the two parts will consist of:
- an online quiz with yes/no, multi-choice and open questions, assessing the student’s knowledge of the topic;
- a small team project, where students will apply the skills and knowledge of the course to preprocess, implement and / or evaluate an infographics and/ or a dashboard.
The final exam will be passed with a minimum grade of 18/30.
The course provides an overview of the main theories, concepts, principles, techniques, heuristics and tools for the design, development and evaluation of interactive data graphics. The student will acquire the knowledge and skills related to:
Objective 1 (O1) – knowledge of theory and principles:
1) Theories of perception, cognitive biases, notions of semiotics and pragmatics;
2) Data types and physical signals (pre-attenptive selection, color theory, gestalt theory, and the like);
Objective 2 (O2) – knowledge of basic concepts and models
3) The grammar of data graphics and its application to encoding and decoding of visual information;
4) Classifications and taxonomies of data graphics;
5) Main design principles;
6) Models and heuristics for a proper interaction design of visual interfaces;
Objective 3 (O3) – Practice, applications and evaluation of concepts and models:
7) Tools and resources for the advanced design of data graphics, static and interactive infographics, and dashboards;
8) Main techniques for choosing and applying the best qualitative and quantitative evaluation test to data graphics and interactive dashboards
9) Notions of visual information literacy and statistical techniques for its evaluation;
Objective 4 (O4) – Soft skills
10) Communication skills related to the acquisition and application of the visual language of data
11) Evaluation skills related to the quality assessment of visual artifacts and tools
The following topics will be touched and discussed into depth:
(O1 – 12 h)
1) Theories of perception, cognitive biases, semiotics and pragmatics. Basic notions of:
a. The Visual system (2h);
b. Cognitive models of attention, perception, action for decision-making (2h);
c. Cognitive biases (2h);
d. Principles of semiotics (2h)
2) Data types and physical signals (pre-attentive selection, color theory, gestalt theory, and the like). Basic notions of:
- Color theory (1h)
- Gestalt theory (shape) (1h)
- Information, Communication and Visual language (2h);
(O2 – 13h)
3) The grammar of data graphics and its application to encoding and decoding of visual information:
a. Visualization syntax (2h);
b. Preprocessing (1h);
c. Data codification (2h);
d. Data relations codification (2h);
e. Time and space constraints (1h);
f. Operands, operators (1h).
4) Classifications and taxonomies of data graphics. Main classifications, also in relations with data types and data complexity
5) Main design principles:
a. human-data interaction (1h);
b. functional design (1h);
c. the task-data-chart models (1h);
d. the invariants model (1h);
(O3 – O4 – 23h)
6) Models and heuristics for a proper representation and interaction design of visual interfaces (5h):
a. Geospatial visualizations (1h);
b. Multivariate visualizations (2h);
c. Hierarchical visualizations (1h);
d. Dynamic hypergraphs (1h).
7) Tools and resources for the advanced design of single charts, static and interactive infographics, and dashboards. Overview, demo, practical examples and use of the following tools (10h):
a. Tableau (2h);
b. Knime (2h)
c. Vega and Vega light (2h);
d. Plotly (2h);
e. D3.js (1h)
f. Other resources (1h)
8) Main concepts and techniques for choosing and applying the best qualitative and quantitative evaluation test to data graphics and interactive dashboards (4h):
a. User study (1h);
b. Psychometric questionnaires (2h);
c. Application of validated indices and scales (1h).
9) Notions of visual information literacy and statistical techniques for its evaluation (4h):
a. the map, observe, compute, reason, infer, and explain cycle (2h);
b. the construct validation (1h);
c. the construct evaluation (1h).
The course consists of lessons (48 hours).
Lessons deal with the overall set of topics listed above using conceptual, formal descriptions and with the support of demo, case studies, on line resources, and small tasks given to students as exercise and further investigation. Open discussions with students during lessons are encouraged.
The teacher receives by appointment, upon request via e-mail to angela.locoro@uninsubria.it. The teacher only replies to e-mails signed and from the student.uninsubria.it domain.