- Details
- Category: Discipline
- Andreotta, M., Nugroho, R., Hurlstone, M. J., Boschetti, F., Farrell, S., Walker, I., & Paris, C. (2019). Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behavior research methods, 51(4), 1766-1781. https://doi.org/10.3758/s13428-019-01202-8 Morettin, P. A., & Singer, J. M. (2020). Introdução à Ciência de Dados. Fundamentos e Aplicações. https://www.ime.usp.br/~jmsinger/MAE0217/cdados2020jun03.pdf Vasconcelos, J. B., & Barão, A. (2017). Ciência dos dados nas organizações. http://hdl.handle.net/10884/1424
- Computer Applications for Data Science
- 6382
- 23554
- Advanced Data Science
- IPLUSO6382-23554
- 2
- 5
- 0
- 4
- Não
- Português
- The course will be developed through theoretical and practical classes, combining the presentation of fundamental concepts with the resolution of applied exercises and the analysis of case studies, from the perspective of Service Learning (SL) in the academic community. Active learning methodologies will be promoted, encouraging student participation in the exploration of data sets, discussion of results, and critical reflection on the analytical processes used.
- Mandatory
- Knowledge: Students are expected to acquire knowledge of the fundamental principles of data science, particularly with regard to ethical processes, data collection, organisation, analysis and interpretation from different contexts, with a view to problem solving. Skills: Students should develop skills to structure data sets, select appropriate methods of analysis, interpret results and communicate conclusions in a clear and reasoned manner, using analytical approaches appropriate to the context of the problem under study. Competencies: At the end of the course, students should be able to apply methodologies (individually or in combination) to solve specific problems, support decision-making processes based on empirical evidence, and act critically and responsibly in the use of data, respecting ethical and legal principles.
- Introduction to applied data science; Ethics, privacy, and data protection (GDPR) in information collection and processing; The role of data in problem solving; Quantitative data collection through questionnaires: types of questions, response scales, common errors in question formulation, pilot testing, and instrument validation. Methods of qualitative analysis of online and offline data: coding, categorisation, identification of patterns and themes in language; Computer support for analysis using specialised software for organising and exploring data; Integration of qualitative and quantitative data for comprehensive results; Exploratory analysis of mixed data; Interpretation and communication of analytical results; Application of the methods studied to case studies in digital environments.
Reflexão escrita, dia 12/11/2025, ponderação de 25%;
Teste de avaliação, dia 14/01/2026, ponderação de 25%;
Projeto final (de grupo), entregar e apresentar dia 21/01/2026, ponderação de 25%;
Participação ativa nas dinâmicas em sala de aula durante a UC, ponderação de 25%
- Semestral
- The course aims to deepen knowledge and skills in the field of data analysis and interpretation, especially from organizational, social and digital contexts. Content related to database structuring and management is covered, as well as the application of advanced mixed analysis methods, with qualitative and quantitative data, and the effective communication of the results obtained. It is intended that students develop the ability to design, implement, and evaluate analytical processes aimed at solving complex problems and supporting decision-making, promoting a critical, ethical and reasoned approach to the use of data.
- Details
- Category: Discipline
- Han, J., Kamber, M., Pei, J. (2012). Data Mining - Concepts and Techniques, Elsevier Gama, J., at al (2017). Extração de conhecimento de Dados – Data Mining. Edições Sílabo
- Information Systems Management
- 6030
- 15430
- Data Mining
- IPLUSO6030-15430
- 2
- 5
- 0
- 4
- Não
- Português
- Use of digital analytics apps and plataforms in support to the learn process, such as: - Microsoft Power BI - SAS Viya for Learners - Linguagem Python - Knime, RapidMiner, Orange
- Mandatory
- This curricular unit aims to address the process of knowledge discovery in databases and the most common methodologies in Data Mining; It is intended that students understand the possible tasks of Data Mining, namely classification, forecasting, trend analysis (time series), grouping, sumarization (and visualization) or association; It is also intended to approach a set of techniques generally used in the implementation of Data Mining, such as decision trees, association rules, linear regression, artificial neuronal networks, genetic algorithms or Bayes networks; Another important goal is the use of an online platform for the application of the theoretical concepts.
- 1. Introduction to Data Mining (6h) - Fundamental concepts: what is Data Mining - Differences between Data Mining, Big Data, Business Intelligence and Machine Learning - The Knowledge Discovery in Databases (KDD) process - Real-world use cases in various areas (healthcare, retail, finance, etc.) 2. Data Preparation and Exploration (6h) - Data types and data quality - Cleaning, transformation and normalization - Exploratory analysis: basic statistics, histograms, boxplots - Sampling techniques 3. Data Exploration Techniques – Part I (9 pm) 3.1 Classification and Regression (9h) 3.2 Clustering (6h) 3.3 Membership Rules (6h) 4. Model Validation and Evaluation (6h) - Training/test split, cross-validation- Overfitting and underfitting- Confusion Matrix, ROC/AUC curves 5. Tools and Workflows in Data Mining (6h) - Presentation of graphical tools: KNIME, RapidMiner, Orange - Creation of visual Data Mining pipelines- Integration with external data sources
Descrição dos instrumentos de avaliação (individuais e de grupo), trabalhos práticos, testes teóricos, projetos e respetivas ponderações na nota final.
Descrição Ponderação Projeto prático 1 (Individual) 25% Projeto prático 2 (individual) 25% Projeto Final (de grupo) 20% Teste teórico final (individual) 20% Assiduidade 10% - Semestral
- The fundamental goal of the Data Mining discipline is to give the student skills in transforming data into information to support decisions, in the context of large databases. Data Mining tools aim to identify future behaviors and trends, supporting the proactive and knowledge-based decision process. They can also answer business questions whose solution has traditionally been very complex from a computational point of view. Thus, this course deals with the themes and issues normally associated with the designations of Data Mining or Knowledge Discovery. In this course, the main methodological aspects of Data Mining will be presented, as well as the most important tools used. The practical component is one of the fundamental aspects of the discipline, so the ability to translate knowledge into practical actions and analysis decisions is particularly valued.
- Details
- Category: Discipline
- Accounting and Finance
- 7157
- 27490
- Sustainability Report
- IPLUSO7157-27490
- 3
- 5
- 0
- 4
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Commercial Management
- 6814
- 24360
- Presentation and Negociation Techiques
- IPLUSO6814-24360
- 2
- 4
- 0
- 4
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Gonzalez, R. & Woods, R. E. (2017). Digital image processing. 4th Ed. Pearson Brown, B.H. et al. (1999). Medical physics and biomedical engineering. Bristol: Institute of Physics Publishing. Rangayyan, R. (2004). Biomedical image analysis. Washington: CRC Press Bailey, D.L., Humm, J.L., Todd-Pokropek, A., Aswegen, A. (2014). Nuclear Medicine Physics – A Handbook for Teachers and Students. IAEA Cherry, S.R., Sorenson, J.A., Phelps, M.E. (2012). Physics in Nuclear Medicine. 4th edition. Elsevier
- Medical Imaging and Radiotherapy
- 4097
- 19637
- Medical Image Processing
- IPLUSO4097-19637
- 2
- 5
- 0
- 4
- Não
- Português
- The course is expository, demonstrative, participatory, and involves problem-solving. The course unit can be assessed through continuous evaluation or a final evaluation. The final grade (CF) for the course through continuous evaluation is obtained from the grades obtained in the Radiology module (50%) and the Nuclear Medicine module (50%). In each module, the content is evaluated in a Theoretical component (50%) and a Practical component (RAD + NM). The minimum grade at each assessment point is 9.5 points.
- Mandatory
- Realize the general concepts of image formation. Understand the application of the general concepts of image formation in the reading and visualization of radiographic, metabolic and image fusion images. Understand the physical principles of image construction and their diagnostic relationship. Understand and know how to evaluate the entire process of building radiographic and nuclear medicine images and the fundamental tools for their acquisition. Stimulate the discussion and criticism of radiographic images and nuclear medicine. Demonstrate knowledge of the technology involved in image acquisition and relate it to radiographic and nuclear medicine imaging. Know advanced signal and image processing tools for both clinical and research areas in Medical Imaging and Radiotherapy.
- Radiologic Imaging Formation of the radiological image: definition of the image as a signal Signal and image processing: image presentation techniques Image reconstruction Metabolic Imaging (Nuclear Medicine) Principles of metabolic imaging: pharmacokinetic component and signal statistics Signal and image processing: planar image presentation techniques (2D) Image Reconstruction Tomographic image reconstruction techniques (3D), filters, segmentation and alignment of tomographic slices Quantitative metrics: manual and automatic/semi-automatic quantification Image Fusion (Hybrid Imaging) Image fusion general principles Hybrid vs. images coming from different equipment Technical aspects of image fusion Clinical utility Main clinical uses
Descrição
Data limite
Ponderação
Módulo de Radiologia:
- Avaliação Teórica (50%)
- Avaliação Prática (50%)
no final do módulo
50%
Módulo de Medicina Nuclear:
- Avaliação Teórica (50%)
- Avaliação Prática (50%)
no final do módulo
50%
Em cada um dos módulos podem ser aplicados mais do que um momento de avaliação, se se justificar, sendo que nesse caso, a nota mínima por momento de avaliação é de 8,0 valores e a nota mínima por componente (T ou P) por módulo (RD ou MN) é de 9,5 valores.
- Semestral
- The CU includes the treatment, processing and reconstruction of medical images in the context of radiological, metabolic and hybrid imaging. It is intended to give students tools for understanding the process of reconstruction and processing of medical images in Radiology and Nuclear Medicine, accompanied by practical classes where this process is put into practice.
