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
| Modules | Credits | TAF | SSD |
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Compulsory courses for Embedded & IoT SystemsCompulsory courses for Smart systems &data analytics2° Year activated in the A.Y. 2021/2022
| Modules | Credits | TAF | SSD |
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Compulsory courses for Embedded & IoT SystemsCompulsory courses for Robotics systemsCompulsory courses for Smart systems &data analytics| Modules | Credits | TAF | SSD |
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Compulsory courses for Embedded & IoT SystemsCompulsory courses for Smart systems &data analytics| Modules | Credits | TAF | SSD |
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Compulsory courses for Embedded & IoT SystemsCompulsory courses for Robotics systemsCompulsory courses for Smart systems &data analytics| Modules | Credits | TAF | SSD |
|---|
3 courses to be chosen 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 (2020/2021)
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 outcomes
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.
Program
* Introduction to data visualization: motivation, visualization problems, tasks and objectives, credibility, ethics, and aesthetics of data visualizations
* Color and perception, cognition, marks and channels
* Guidelines for visualization design
* Data, models and data encoding, filtering, aggregation, multidimensional data
* Mapping data on a color scale
* Charts and their visualization
* Maps, text display, graphs, tabular data display, graphs and networks display
* Spatial layout management, view manipulation, focus and context
* Interaction, UI elements, animation, dashboard, multiple views
* Scientific visualization, image and volume visualization
* Design evaluation
* Practical applications using visualization tools
Examination Methods
Students will be assessed by an oral exam and evaluation of laboratory projects. The final grade will derive 50% from the lab project evaluation and 50% from the oral evaluation.
