- Details
- Category: Discipline
- SÁ E MELLO, Alberto de - Direito do Trabalho para Empresas. Coimbra: Almedina, 2023.
- Accounting and Management
- 3564
- 18864
- Labor Law and Labor Relations
- IPLUSO3564-18864
- 1
- 5
- 0
- 4
- Não
- Português
- Teaching a legal subject on an Accounting and Management course requires a strong sense of practicality and a clear link to the reality of the professional world in question. Therefore, and within the framework of learning the fundamental principles and rules already set out, the use of concrete examples facilitates the acquisition of the competences that make up its transversal domain (also as set out above). There are two written tests and attendance and participation are taken into account (including the oral presentation of work), as follows: First test: 30 per cent of the final assessment. Second test: 30 per cent of the final assessment. Attendance and participation: 40% of the final assessment.
- Mandatory
- Knowing, interpreting and mastering the macro legal-normative framework of labour relations. Know, interpret and master the key concepts and issues in the field of the labour market.
- Law, Labour Relations and Labour Law. Constitutional Labour Law. International labour law. European labour law. The Employment Contract as a paradigm of the Labour Relationship: the Individual Employment Relationship. Labour Contract: notions, characteristics and fundamental elements of the regime. Collective Labour Relations. Social dialogue and tripartism. The Economy and the Labour Market. Employment and Labour. Labour Administration. Labour Jurisdiction.
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
08-04-2026
40%
Teste de avaliação
20-05-2026
40%
Provas orais.
27-05-2026
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...
- Semestral
- The UC Labour Law and Labour Relations appears in the course in the context of an overall objective of providing students with the macro legal-normative framework that is indispensable for structuring the fundamental bases for building an academic path that is successful from the point of view of satisfying the objectives of acquiring and mastering the theoretical and practical tools that are indispensable for adequate and high-quality professional practice in the area of business management. The UC Labour Law and Labour Relations should be a fundamental axis of the CTESP in Accounting and Management, precisely because the legal framework for activities in the field of accounting and business management, which is so exhaustive and omni-comprehensive, does not relieve the professional of the need for its knowledge and basic mastery
- 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
