Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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Compulsory activities for Embedded & Iot SystemsCompulsory activities for Smart Systems & Data Analytics2° Year activated in the A.Y. 2023/2024
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Compulsory activities for Embedded & Iot SystemsCompulsory activities for Robotics SystemsCompulsory activities for Smart Systems & Data Analytics| Modules | Credits | TAF | SSD |
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Compulsory activities for Embedded & Iot SystemsCompulsory activities for Smart Systems & Data Analytics| Modules | Credits | TAF | SSD |
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Compulsory activities for Embedded & Iot SystemsCompulsory activities for Robotics SystemsCompulsory activities for Smart Systems & Data Analytics| Modules | Credits | TAF | SSD |
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3 modules among the followingLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Data Visualization (2022/2023)
Teaching code
4S009024
Credits
6
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
- Data visualisation of the course Master's degree in Data Science
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
The teaching is organized as follows:
Teoria
Laboratorio
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
Oral exam, project presentation and homework evaluation
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
50% oral evaluation, 40% project evaluation 10% homework evaluation
Exam language
Inglese
