Instance: 2020/2021 - 1S
Cycles of Study/Courses
Teaching Staff - Responsibilities
Teaching - Hours
|Theoretical and practical :
Portuguese and english
Obs.: All course material will be given in English
This course will introduce the concepts of Data Visualization with a focus on Data Science and Visual Analytics. It spans over a multi-disciplinary domain that combines data visualization with machine learning and their automated techniques to help people make sense of data.
Students will be introduced to the design of visual representations that support tasks that take the user from raw data into insights. Topics include basic concepts of information visualization; visual analytics of evolving phenomena; analysis of spatial and temporal data sets; visual social media analytics; and the visual analytics of text and multimedia collections.
Students will prototype visual analytics applications using existing frameworks and libraries, coupling machine learning and visualization methods. Students will gain competency in performing data analysis through visualization tasks in different application domains.
- Create graphs appropriate to the type of context and problem to be explored
- Create and enhance graphics using R and Python tools
- Integrate graphics developed in R / Python into interactive environments.
- Design and develop a Big Data access dashboard for interactive manipulation of multiple graphs.
Learning outcomes and competences
- Understand and know how to apply the key concepts involved in creating an R and Python chart using your latest libraries.
- Explain the standard methods of automatic learning and data mining and their particularities for visualization purposes.
- Explain the methods of visualization and visual analysis and the differences between them.
- Describe the limits of machine learning and data mining methods to solve data analysis problems and when visualization methods are possible.
- Create elegant and simple strategies for solving data analysis problems by integrating visualization with machine learning and data mining methods and justifying choices
- Perform data analysis tasks on large data sets to visually discover data patterns and formulate hypotheses
Pre-requirements (prior knowledge) and co-requirements (common knowledge)
Programming experience, ideally with R or Python.
- Data representation and transformation
- Data visualization overview
- Visual analysis: motivation, models, challenges and analytical thinking.
- Key elements in graphical data visualization
- Chart types, problem fit, and graphic integrity
- Graphing for univariate and multivariate data
- Conditional, temporal and multi-aggregate value graphs
- Visual data mining: dimensionality reduction for cluster results visualization and visualization
- Visual analysis for text and multimedia collections
- Visual analysis for spatial and temporal data sets
- Panels for interactive manipulation of simultaneous graphic views
M. O. Ward, G. Grinstein, D. Keim; Interactive Data Visualization: Foundations, Techniques, and Applications, CRC Press, 2015 (Main book)
Hadley Wickham; ggplot2: Elegant Graphics for Data Analysis. ISBN: ISBN-10: 0387981403 (Main book)
L. Wilkinson; The Grammar of Graphics, 2005. ISBN: ISBN-10: 0387245448
Eduard Tufte; The Visual Display of Quantitative Information, 2001. ISBN: ISBN-10: 0961392142
William Cleveland; The Elements of Graphing Data . ISBN: ISBN-10: 0963488414 (Historical perspective)
Comments from the literature
Main articles used in the course:
- Sacha, A. Stoffel, F. Stoffel, B. C. Kwon, G. Ellis and D. A. Keim, "Knowledge Generation Model for Visual Analytics," in IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1604-1613, 2014.
- Endert, W. Ribarsky, C. Turkay, B.W. Wong, I. Nabney, I. D. Blanco, and F. Rossi, “The State of the Art in Integrating Machine Learning into Visual Analytics”, Computer Graphics Forum, vol. 36, pp. 458-486, 2017.
Teaching methods and learning activities
During the lectures, it will be used the expository method, being presented an organized view of the topics of the program, as well as practical examples of their application.
There will be monitoring of the implementation of practical work and the creation of execution milestones.
Some lectures will include Individual and class analysis of scientific articles and texts as well as presentation and discussion of classwork.
Each 3-hour class will be divided into a theoretical part, discussion, and a practical part to apply the knowledge in real-world cases.
Distributed evaluation with final exam
|Apresentação/discussão de um trabalho científico
|Trabalho prático ou de projeto
Amount of time allocated to each course unit
|Apresentação/discussão de um trabalho científico
|Elaboração de projeto
|Frequência das aulas
Eligibility for exams
Execute all mandatory assessment items:
- Mini-test (MT)
- Projet proposal (PP)
- Project development (TDP)
- Projet final presentation (AF)
- Final exam (EF)
Final exam with a grade equal or greater than 40%.
Final grade equal or greater than 9.5.
Calculation formula of final grade
Final grade = 0,1*MT + 0,1*PP + 0,4*TDP + 0,1*AP + 0,3*EF
The course project is the student opportunity to develop skills and expertise in Data Visualization. The course project is designed to take students through all stages of development, from coming up with a new idea for a data visualization to a visual analytics application to designing, implementing, testing, and demonstrating the application. For manageability, the project is divided into different deliverables: proposal, implementation, and project demonstration.
Only the on the "Exame Final" component.