Visualization and Data Analytics
• What is Visual Analytics (VA).
• The role of interaction in VA.
• Data Visualization’s role in Exploratory Data Analysis (EDA) and the design of machine learning models.
• The concept of Visual Variable.
• VA techniques for multivariate data, spatial data and time dependent data.
• The main general components of VA systems.
• Methodologies for comparison and evaluation of VA techniques and systems.
• Choose the VA techniques most appropriate to a data set and objectives.
• Use a VA system to explore and view one or more datasets.
• Design and implement a ADV solution for a data class and for a set of exploration objectives.
• Understand the multidisciplinary nature of this area and understand its relationship with other areas of knowledge and engineering.
• Explore the experimental nature of VA
João Carlos Gomes Moura Pires
Weekly - 4
Total - 60
General progamming skills
Visualization Analysis & Design, Tamara Munzner, 2015, ISBN: 9781466508910
ISBN (e-Book): 9781498707763
- Interactive Data Visualization: Foundations, Techniques, and Applications, Second Edition. Matthew O. Ward, Georges Grinstein, Daniel Keim, 2015, ISBN 9781482257373
How Charts Lie: Getting Smarter about Visual Information. Alberto Cairo, 2019
In the lectures the course content is presented, illustrated with application examples and references to related work. The laboratory classes are intended for specification, development and presentation of the project that deals with topics presented during the lectures.
The evaluation of the course consists of five elements: two mid-term written individual tests and one project, with several phases (specification, state of the art and code/interface) that together account for a project to develop a interactive data visualization application, using real data.
The evaluation of the course includes 3 elements: two individual tests carried out throughout the semester and 1 project, with several phases (specification, article, code / interface and oral), which correspond to a team project (3 students) of an interactive data visualization solution.
Calculation formula of the final classification (rounded to the nearest integer):
Final classification = 25% Test1 + 25% Test2 + 50% Project
where the Tests and the Project grades are rounded to one decimal place.
Course approval requires the following classifications:
(average (Test1; Test2)> = 9.5) and Project> = 10
Students who obtained Project > = 10 and did not pass the tests will be able to take an exam, the grade of which will replace the tests grade in the calculation of the final grade.
The tests and exam will be carried out preferably in person and will be without consultation.
Introduction to Data Visualization
What Is Visualization?
Relationship between Visualization and Other Fields.
The Visualization Process.
Human Perception and Information Processing.
Semiology of Graphical Symbols.
The Visual Variables.
Visualization Techniques for Spatial Data
Visualization Techniques for Geospatial Data
Visualization Techniques for Time-Oriented Data
Visualization Techniques for Multivariate Data
Visualization Techniques for Trees, Graphs, and Networks
Text and Document Visualization
Interaction Concepts and Techniques
Interaction Operators, Operands and Spaces (screen, object, data, attributes)
Visualization Structure Space (Components of the Data Visualization)
Designing Effective Visualizations
Comparing and Evaluating Visualization Techniques
Systems Based on Data Type
Systems Based on Analysis Type
Text Analysis and Visualization
Modern Integrated Visualization Systems
Research Directions in Visualization
Programs where the course is taught: