Teaching code

4S009024

Credits

6

Coordinator

Andrea Giachetti

Language

English

Also offered in courses:

• Data visualisation of the course Master's degree in Data Science
• Data visualisation of the course Master's degree in Data Science

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Courses Single

Authorized

Moodle Seminars 0

The teaching is organized as follows:

Learning objectives

The course aims to provide tools for the effective visualization of heterogeneous data. A conceptual model of visualization application will be presented, and the main problems and techniques of information and scientific visualization applied to different types of data will be introduced. The course will address the perceptual and technical problems related to data modeling and organization and graphic rendering. Examples and guidelines for effective visualization design in different contexts will be provided. At the end of the course the student will have to demonstrate knowledge and understanding of the basic principles and main problems of visualizing abstract and concrete data. He must be able to design scientific and information visualization applications. This knowledge will provide the student with the ability to: i) independently evaluate algorithms and visualization software knowing how to choose the correct framework for each different task; ii) to apply visualization techniques in different industrial scenarios. At the end of the course the student will have to show that he is able to design, evaluate and use visualization tools for data analysis research and communication.

Prerequisites and basic notions

Basic knowledge of statistics

Program

Introduction to data visualization: motivation, visualization problems, tasks and goals. Design evaluation
Color and perception, Mapping of data on a color scale, Marks and channels
Guidelines for visualization design, ethics in visualization
Data, models and data encoding, filtering, aggregation, multidimensional data
Graphs and their visualization
Tabular data visualization, graph and network visualization Maps, scientific visualization, image and volume visualization Spatial layout management, view manipulation, focus and context
Interaction, user interface elements, animation, dashboards, multiple visualizations
Prototyping using visualization packages in python

Bibliography

Didactic methods

Lectures and guided examples in Python

Learning assessment procedures

Written exam, project presentation and homework evaluation.
The written exam consists of 4 open questions on the theory program.
The assignments will consist of exercises on the practical part to be delivered.

Evaluation criteria

Students will have to demonstrate understanding of the design problems of a visualization application, knowing the main types of data and encodings, the problems of visual mapping and related human factors, the main visualization techniques used for the various types of data. Students will be expected to demonstrate the practical ability to design effective visualizations of data using basic libraries and following the rules and principles of design.

Criteria for the composition of the final grade

60% written exam evaluation, 40% homeworks evaluation

Exam language

Inglese