Advanced Training in Web Data and Visualizations
Cod: 23044
Department: DCET
ECTS: 15
Scientific area: Technology and Web Systems
Total working hours: 405
Total contact time: 100

This learning unit aims at providing spaces for reflection and study about complexity of Web data analysis and its fulfilment in new contexts of visual and spatial interaction.

Web Data
Web Visualization
Web Data Search and Analysis

When concluding this learning unit students shall be able to:
- Identify issues in search, collection, processing, retrieval, visualization, analysis, and interpretation of information from Web data;
- Describe concepts, methods, and techniques for formal analysis of social networks;
- Describe the main concepts, models, and techniques associated with the Semantic Web and knowledge extraction from the Web;
- Discriminate the main techniques and tools for Web events, statistical analysis, computation & simulation, and data visualizations;
- Discriminate and reflect upon technologies and issues in information visualization in face of growing amounts of data;
- Describe the concepts, models, and techniques associates with the development of digital culture and art artefacts on the Web.

This learning unit is divided in different topics embracing relevant state-of-the-art fields within the area of web data and visualization, which are organized in curricular modules of 2 credits ECTS each.
In following are listed some of these topics:Metaverse Technologies
-Learning Analytics and Educational Data Mining
-Natural Language Processing
-Information Visualization
-Art Artifacts and Digital Culture
-Narratives and Multimedia Games
-Experimental Statistics and Web Data Analysis
-Applied Deep Learning
-Metaverse Technologies

  • ALLEMANG, D., HENDLER, J. (2011), Semantic Web for the Working Ontologist, 2nd Edition: Effective Modeling in RDFS and OWL, Morgan Kaufmann.
  • BUETTCHER, S., C.L.A. CLARK, G.V. Cormack (2010), Information Retrieval: Implementing and Evaluating Search Engines, The MIT Press.
  • HAN, J. (Author), M. KAMBER, J. PEI (2011), Data Mining: Concepts and Techniques, 3th edition, Morgan Kaufmann Series in Data Management Systems.
  • MONTGOMERY, D.C. (2013). Design and Analysis of Experiments. 8th Edition, John Wiley & Sons, ISBN: 978-1-118-14692-7 (print) / 978-1-118-32425-7 (ebook)
  • NEWMAN, M. (2010), Networks: An Introduction, Oxford University Press, USA.
  • OLIVEIRA, T.A., OLIVEIRA, A., Pérez-Bonilla, A. (2012). Data Mining and Quality in Service Industry: Review and some applications. In "Decision Making in Service Industries: A Practical Approach", Taylor & Francis.

WARE, C. (2013). Information visualization: perception for design. Elsevier.


The evaluation of this learning unit includes a dimension of a continuous nature, based on assessing the quality of work summaries realized within each module; of the discussion carried out, online, in virtual class; and also on the individual project development of a artifact that renders the concrete knowledge achieved in context of up to three modules chosen by the student.
Throughout the semester, students will be integrated into research groups within the modules, participating in the analysis of problems and developing solutions and prototypes in order to find relevant results or new artefacts that render concrete knowledge achieved in each module.

Em função da natureza mista da unidade curricular o processo de ensino/aprendizagem observa uma abordagem de aprendizagem  teórico-prática colaborativa online, em turma virtual, que se baseia na realização de trabalhos práticos individuais e em grupo, cujos resultados são apresentados tanto online como em sessão presencial, para assegurar a complementaridade recíproca entre a teoria e a prática.