- 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
- AMARAL, Diogo Freitas do. Curso de Direito Administrativo. Volume II. 4ª Edição. Reimpressão. Almedina. 2020 FURTADO, Miguel. Progressividade ou Proporcionalidade em sede de IRS, no âmbito da justiça social? Revista Minerva Universitária. 2024 FURTADO, Miguel. Direitos Fundamentais: Com ou Sem Hierarquia? Revista Minerva Universitária. 2023 FURTADO, Miguel. Estado Fiscal enquanto Sinónimo de Liberdade. ISG. 2022 FURTADO, Miguel. Caraterização da Administração Pública. Universidade Lusófona. 2020 FURTADO, Miguel. Hierarquia das Leis. ISG. 2022 NABAIS, José Casalta. Direito Fiscal. 11ª edição, Almedina. 2019 ROCHA, Joaquim Freitas. Lições de Procedimento e Processo Tributário. 2ª edição, Almedina. 2019 SOUSA, Domingos Pereira de Sousa, Direito Fiscal e Processo Tributário, Coimbra Editora. 2013 Constituição da República Portuguesa Código do Procedimento Administrativo Lei Geral Tributária Código do Procedimento e do Processo Tributário
- Accounting and Management
- 3564
- 18869
- Declarative Procedures
- IPLUSO3564-18869
- 2
- 5
- 0
- 4
- Não
- Português
- Teaching methodologies: a) Interactive classes, with oral, textual and graphic presentation of the subject, accompanied by participation and discussion of the topics taught. b) Research and individual/group work. c) Resolution of practical cases. Knowledge assessment method: The assessment of knowledge will be based on the weighting of three aspects: a) Preparation and presentation of works and respective debate in class: 20% b) Class participation, including debates on the presentation of the work, resolution of practical cases and individual oral tests: 20%. Attendance is a prerequisite for continuous assessment. Note: The student must be present in 75% of classes c) Taking a main written test (60%): January 8th 2025 at class time.
- Mandatory
- - Understand the way in which the bodies that exercise public powers act, through the identification of the main legal principles that shape their actions; - Know the various bodies and entities with which it will be necessary to make professional contacts as well as their modes of operation, organization and connection; - Mastering and handling the respective legislation related to the various stages of the declarative procedures; - Know and know how to move in the various stages of the declarative procedures; Know, know how to discern and use the necessary acts and documentation within the scope of declarative procedures; - Understand and know how to apply the various guarantees of citizens and taxpayers in their favor in conflict with the Public Administration.
- - Procedures and respective constitutional principles; - Principles of Public Administration and Administrative Procedure; - Administrative Procedure, respective phases and citizens' guarantees; - Tax constitution, tax system and taxes - the tax in particular: - Legislation and Public Tax Administration; - Tax procedure: concept, object, subjects, general principles and phases; - Settlement procedure and the tax act; - Tax inspection procedure; - Taxpayers' guarantees within the scope of the tax procedure; - Connection between procedure and tax litigation; - Major tax declaration procedures.
Descrição dos instrumentos de avaliação (individuais e de grupo) ¿ testes, trabalhos práticos, relatórios, projetos... respetivas datas de entrega/apresentação... e ponderação na nota final.
Exemplo:
Descrição
Data limite
Ponderação
1º Teste de avaliação
2º Teste de avalição
Apresentação de trabalho
Participação em aula
19-11-2025
14-1-2026
--------------
---------------
30%.
30%
20%
20%
A participação em aula, incluindo a apresentação de um trabalho e discussãodo mesmo, será objecto de ponderação para a nota final da disciplina.
Ao longo do semestre
Adicionalmente poderão ser incluídas informações gerais, como por exemplo, referência ao tipo de acompanhamento a prestar ao estudante na realização dos trabalhos; referências bibliográficas e websites úteis; indicações para a redação de trabalho escrito...
- Semestral
- It is a UC that will enable students to know and understand the procedural steps of declaratory scope that they will need to carry out in the development of their functions, both at the administrative and tax levels. This curricular unit is therefore an indispensable tool for future professionals to find themselves fully equipped to be able to perform their day-to-day work within the scope of the functioning and organization of the respective Public Administration, with special priority for the tax nature, something that in its This majority is a relevant gap for those who are already working in the area of ¿¿the course.
- Details
- Category: Discipline
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate Data Analysis (8th ed.). Cengage Learning. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2nd ed.). O'Reilly Media.
- Computer Applications for Data Science
- 6382
- 23559
- Analysis and Treatment of Multivariate Data
- IPLUSO6382-23559
- 2
- 5
- 0
- 4
- Não
- Português
- Project-Based Learning (PBL): Promotes practical application, allowing students to work on real datasets, proposing and implementing solutions to concrete problems. Interactive Coding Platforms: Using tools such as Jupyter Notebooks or RStudio Cloud to facilitate experimentation and real-time data visualization. Peer-to-Peer Discussion Groups: Fostering the exchange of ideas and collaboration, allowing students to learn from each other and share different perspectives.
- Mandatory
- Knowledge: Students will acquire a deep understanding of the nature and complexity of multivariate data and the statistical techniques and algorithms used in their analysis. Skills: They will be able to perform exploratory analyzes of multivariate data, identifying patterns, correlations and anomalies. Additionally, they will develop capabilities to apply dimensionality reduction methods, such as PCA and t-SNE, and clustering and classification techniques. Skills: Students will be proficient in specific tools and software for processing multivariate data. They will also gain the ability to effectively communicate the results of their analyses, transforming complex data into actionable insights and data-driven solutions to real-world problems. Overall, they will be able to make informed decisions based on analysis of multivariate datasets, making a valuable contribution to any data science team.
- Introduction to Multivariate Data: Basic concepts, types and structures of multivariate data. Exploratory Data Analysis (AED): Multivariate data visualization, outlier detection and statistical description. Correlation and Causality: Differences, calculation methods and implications. Dimensionality Reduction: Techniques such as Principal Component Analysis (PCA) and t-SNE. Clustering and Segmentation: Algorithms such as K-means and DBSCAN. Multivariate Classification: Introduction to models such as Multinomial Logistic Regression and Support Vector Machines. Validation and Interpretation of Models: Evaluation methods, metrics and interpretation of results. Practical Applications: Case studies and projects in specific domains, using tools such as R, Python and their specific libraries.
Descrição
Data limite
Ponderação
Teste de avaliação
17-11-2025
30%
Projeto Final
19-01-2026
70%
- Semestral
- The Curricular Unit "Analysis and Processing of Multivariate Data" is an essential component of the professional technical course in Computer Applications for Data Sciences. Within the field of action, this course focuses on the study and manipulation of data sets with multiple variables, exploring the complexities and interrelationships between them. The area of ¿¿expertise encompasses advanced statistical techniques, machine learning algorithms and visualization methods for multivariate data. As for the intervention domain, it addresses both the underlying theory and practical application, using modern software and tools specific to the processing of multivariate data. The relevance of the UC in the study cycle is unquestionable, as understanding and processing multivariate data is a central pillar in data science, allowing students to extract deeper insights and develop more accurate predictive models from complex data sets.
- Details
- Category: Discipline
- Câmara, P. B.; Guerra, P. B. & Rodrigues, J. V. (2016). Humanator XXI Recursos Humanos e Sucesso Empresarial , Edições D. Quixote, 7ª Edição; Rego, A.; Cunha, M. P.; Gomes, J. F. S.; Cunha, R. C.; Cabral-Cardoso, C. & Marques, C. A. (2015). Manual de Gestão de Pessoas e do Capital Humano , Edições Sílabo, 3ª Edição; Sousa, M. J.; Duarte, P.; Sanches, P. G. & Gomes, J. (2006). Gestão de Recursos Humanos, métodos e práticas , Lídel Editora, 4ª Edição.
- Information Systems Management
- 6030
- 523
- Human Resources Management
- IPLUSO6030-523
- 2
- 5
- 0
- 4
- Não
- Português
- Students will work in groups to collect data from companies in one of the human resources areas. The goal is to help students understand how human resources concepts are applied in practice and to gain exposure to business life (small businesses).
- Mandatory
- LO1 - Describe the historical origins of the HR function and frame it within the organisational context. Define HRM and highlight its main objectives/challenges. LO2 - Describe the various activities/functions/responsibilities that may be related to the HR function. LO3 - Reflect on the issues of diversity, discrimination, and harassment in organisations. LO4 - Present the main Human Resources processes and techniques.
- PC1. Basic concepts and perspectives on HRM; PC2. Organisational climate and the importance of communication; PC3. Diversity, discrimination, and harassment; PC4. Recruitment and selection in organisations; PC5. Employee welcome, integration, and socialisation; PC6. Reward and compensation systems; PC7. Training; PC8. Performance evaluation; PC9. Time management and work organisation.
Descrição dos instrumentos de avaliação (individuais e de grupo) ¿ testes, trabalhos práticos, relatórios, projetos... respetivas datas de entrega/apresentação... e ponderação na nota final.
Exemplo:
Descrição
Data limite
Ponderação
Teste de avaliação
A agendar
50%
Trabalho de grupo (Escrito e Apresentação)
A agendar
30%
Participação e atividades nas aulas
20%
Adicionalmente poderão ser incluídas informações gerais, como por exemplo, referência ao tipo de acompanhamento a prestar ao estudante na realização dos trabalhos; referências bibliográficas e websites úteis; indicações para a redação de trabalho escrito...
Para os alunos com o estatuto de trabalhador-estudante ou outro estatuto semelhante, o docente pode solicitar um trabalho extra para complementar a avaliação de conhecimentos da UC.
- Semestral
- The Human Resource Management course syllabus covers the basic theoretical and practical foundations of Human Resource Management in organisations. Its relevance to the study cycle is due to the growing importance of Human Resources in creating competitive advantages for organisations.
