- Details
- Category: Discipline
- Project Management (M) (ISLA-SANTARÉM)
- 7160
- 814
- Traineeship
- ISLA Santarém7160-814
- 2
- 30
- 1340
- 25
- Sim
- Português
- Optional
- Semestral
- Details
- Category: Discipline
- Badger, I. & Menzies, P. (2005). Business Life. London. Marshall Cavendish Education. Brooks, Bessie (2013). A Guide To English Grammar: Conjugation Of Verbs Volume 1, 2 and 3, Xisbris Corporation. Denham, K. & Lobek, Anne (2013). Navigating English Grammar - A Guide To Analyzing Real Language, John Wiley and Sons Ltd. Evans, V., Dooley, J. & Wright, S. (2012).Career Paths: Information Technology, UK. Express Publishing. Swan, Michael & Walter C. (2007). How English Works: A Grammar Practice Book (With Answers), Oxford University Press.
- Information Systems Technology and Programming
- 2045
- 12561
- English
- ISLA Santarém2045-12561
- 1
- 5
- 0
- 25
- Não
- Português
- In presence: 1. Theoretical/practical classes: presentation of concepts; discussion of programme contents; textual interpretation; resolution of grammatical and lexical exercises using digital tools (RED). 2. Practical classes: conversational situations; simulation of real situations; discussion of topics; brainstorming; researching information and presenting/discussing it, using digital tools (AI); solving exercises either in an intranet environment or using paper support. Autonomous: 3. Inverted classroom dynamics with information research for application in the classroom context. 4. Consolidation of the contents taught in class by researching additional information and solving exercises on the different themes and contents.
- Mandatory
- O1. Promote the development of oral and written skills. O2. Mobilize previously acquired linguistic knowledge and apply it in new learning situations. O3. Provide the improvement of the English language, adapting it to the socio-professional context. O4. Use the English language as a work and communication tool. O5. Improve the interpretation of scientific and technological matters. Skills and competencies: C1. Develop autonomy in communication, promoting oral and written expression, as well as mastery of grammatical rules and vocabulary. C2. Be able to apply previous linguistic knowledge to new contexts, adapting what has been learned to varied communication situations. C3. Know and practice vocabulary and technical terms related to your area of work. C4. Actively apply the English language in work tasks and situations, as a means of communication.
- Content 1. Global communication. [O1, O2, O3, O4 and O5; C1, C2, C3, C4]. Content 2 The English language in socio-professional daily life. [O1, O2, O3, O4 and O5; C1, C2, C3, C4]. Content 3 The English language and new technologies. [O1, O2, O3, O4 and O5; C1, C2, C3, C4]. Content 4. Oral and written communication in English. [O1, O2, O3, O4 and O5; C1, C2, C3, C4]. Content 4.1 The formal aspects (phonological, syntactic and lexical-semantic) of the English language system. [O1, O2 and O3; C1, C2 and C3]. Content 4.2 Grammar: verb tenses; If-clauses. [O1, O2 and C1, C2]. Content 4.3 Interpretation and text production. [O1, O2, and O5; C1, C2]. Content 4.4 Planning and development of individual projects in English; written and oral presentation of these projects. [O1, O2, O3, O4 and O5; C1, C2, C3, C4].
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
12-01-2026
50%
Portfolio
Ao longo do ano
15%
Apresentação oral
24-11-2025
35%
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
- NA
- Details
- Category: Discipline
- Beauchamp, T., & Childress, J.F. (2019). Principles of Biomedical Ethics (8ª ed). Oxford University Press. Deodato, S. (2017). A proteção de dados pessoais de saúde. Lisboa. UCP editora. Furtado, T. ,Santos, G., Melo, P., & Maia, R. (2016). Responsabilidade social e ética em organizações de saúde. (2ªed). Rio de Janeiro. Editora IDE Kayaalp. M. (2018). Patient privacy in the era of big data. Balkan Med J. 35(1), 8-17. Knoppers, B., & Thorogood, A. (2017). Ethics and big data in health. Current Opinion in Systems Biology. Matos, F. (2017). Ética na gestão empresarial. (3ªed). São Paulo.Editora Saraiva Ozair, F., Jamshed, N., Sharma, A., & Aggarwal, P. (2015) Ethical issues in electronic health records: A general overview. Perspectives in Clinical Research
- Data and Technology Management in Health
- 7055
- 26681
- Ethics and Privacy in Health
- ISLA Santarém7055-26681
- 1
- 3
- 0
- 25
- Não
- Português
- Met 1 - active methodologies, through work in the classroom (expository, interrogative and active). Met 2 - Reading the recommend bibliography . Solving pratical exercices that have not been solved during practical classes and others proposed by the teacher. These materials and exercises are made available on the plataform.
- Mandatory
- O1. Know the foundations of ethics and discuss the main concepts; O2. Know the distinctions between ethics, morality, bioethics and deontology; O3. Recognise ethical and legal problems in health institutions; O4. Discuss the meanings of autonomy, privacy, and confidentiality and their interrelationships with the reality of care actions developed in health institutions; O5. Know the ethical aspects and rights to data protection in healthcare; O6. Know the main models of deliberation / decision-making; O7. Know the ethical and legal aspects of health research; O8. Know the ethical aspects of using technologies.
- 1. - Fundamentals of Ethics: 1.1 - Definition of concepts (ethics, morality, bioethics and deontology); 1.2 - Person and human dignity; 1.3 Human values. 2. - Ethical and legal values in healthcare institutions: 2.1 - Ethical principles (Principiology of Beauchamp and Chikdress); 2.2 - Equity in access to healthcare; 2.3 - Privacy and confidentiality. 3. - Fundamentals of bioethics: 3.1 - History of bioethics; 3.2 - Models of thought in bioethics 4. - Ethical principles guiding research and health: 4.1 - Ethical principles for medical research involved human beings (Declaration of Helsinki, regulation, directives); 4.2 - Voluntary consent; 4.3.- Benefit/Damage; 4.4 - Distributive justice in conducting medical research. 5. - Ethical implications of using new technologies: 5.1 - Artificial intelligent in health; 5.2 - Telemedicine; 5.3 - Big data; 5.4 - Ethical challenges associated with new technologies and strategies to mitigate risks.
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
17-11-2025
50%
Teste de avaliação
26-01-2026
50%
(...)
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
- .
- Details
- Category: Discipline
- Information Technology Management (ISLA Santarém)
- 1215
- 10784
- Innovation and Entrepreneurship
- ISLA Santarém1215-10784
- 3
- 4
- 0
- 25
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Gama, J. et al, (2017). Extração de Conhecimento de Dados, Sílabo. Han J., Micheline K. e Jian P. (2022). Data Mining - concepts and techniques, elsevier science technology Kirk, Andy (2024). Data Visualisation: A Handbook for Data Driven Design. Sage Publications Ltd Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2025). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. Sharda, Ramesh (2024). Business Intelligence, Analytics, Data Science, and Ai, Global Edition. Pearson Education Limited
- Web Systems and Technology Engineering
- 6159
- 22491
- Data Knowledge Extraction
- ISLA Santarém6159-22491
- 1
- 6
- 0
- 25
- Não
- Português
- The applied methodology is an expository methodology, in the theoretical contents and laboratory practice in the contents of practical application
- Mandatory
- Intended learning outcomes (knowledge, skills and competences to be developed by the students). The objectives of the curricular unit are: -Identify the main techniques, methodologies and knowledge extraction tools from a high volume of data; -Present data mining techniques; -Present the learning models; -Present and use the tools (Data Wharehouse, OLAP, BI and Data mining). At the end of the course unit students should be able to: -Work with database techniques, statistics and machine learning. -Build decision support systems for today's large and medium enterprises. -Recognize the role and importance of data knowledge extraction in the broader context of building decision support systems in the information and knowledge society; -Apply knowledge extraction techniques from large data in real and experimental context.
- 1. Introduction to Business Intelligence, Data Mining, CRISP-DM methodology 2. Data Warehouse and OLAP systems 3. Adaptive Business Intelligence 4. Forecasting and Optimization 5. Data Mining: classification, regression, segmentation 6. Learning Models (e.g., Decision Trees, Neural Networks) 7. Learning Statistics 8. Tools (Data Warehouses, OLAP, BI, Data Mining) 8.1. Data Warehouse - ETL Processes 8.2. Open Source Business Intelligence - OLAP Servers/Clients 8.3. Data Mining - Report Creation, Dashboards 8.4. Analysis of Open Source Business Intelligence Platforms 8.4.1. Pentaho Business Intelligence Server 8.4.2. SpagoBI
Metodologia de avaliação - contínua:
- Trabalho prático (Relatório e projeto); 60%;
- Teste final teórico-prático; 40%;
Todos os estudantes que não tenham concluído com sucesso a avaliação podem realizar um exame final teórico-prático na época de avaliação definida pela instituição.
- Semestral
- The extraction of knowledge, patterns or database trends is an essential element in the construction of decision support systems. It is an area closely linked to database techniques, statistics and machine learning. Some skills to acquire stand out: The importance in extracting data knowledge in the more general context of building decision support systems in the information and knowledge society; Identify some of the techniques, methodologies and tools of knowledge extraction from a high volume of data; Apply knowledge extraction techniques in experimental context.
