- Details
- Category: Discipline
- Não
- The Programming II course focuses on web development, providing students with fundamental knowledge and skills for creating dynamic web applications. Using HTML, CSS, and Python/Django, students will learn to structure, style, and program interactive websites, strengthening essential frontend and backend development practices. The course falls within the area of programming and software engineering, covering the development and maintenance of web applications, frotend and backend, with a focus on creating responsive interfaces, database integration, and the implementation of Information Systems, applying good programming and security practices. This course unit is essential in the study cycle, as it equips students with fundamental skills for the digital market and information management on the web.
- Semestral
Descrição
Data limite
Ponderação
Observações
Projecto de grupo
22-06-2026
70%
Projecto tem 3 entregas ao longo do semestre
Detalhes abaixoDefesa
22-06-2026
15%
Defesa em grupo, mas com avaliação individual Trabalhos individuais
(em aula ou em casa)Vários
15%
Web design 20% N/D
Após aula 4Desenho de página Implementação com HTML e CSS Site dinamico com JS 35% N/D
Após aula 9Eventos e DOM Formulários e validação Armazenamento local Integração com API Boas práticas e testes Entrega / Avaliação 45% N/D
Após aula 15Gestão de utilizadores e autenticação Diferenciação de perfis de utilizador Controlo de sessões Ambiente multi-utilizador Persistência de dados com Base de Dados WebApp Os trabalhos de grupo são desenvolvidos em plataforma Docker, em modelo uniforme disponibilizado no início do semestre. Os alunos são incentivados a colocar os trabalho na plataforma GitHub para facilitar o acesso e interacção com o docente. Na entrega final, apenas será entregue código fonte, submetido em plataforma Moodle
- Introduction to Web Development Frontend, backend, HTTP concepts and client-server model. Frontend Languages: HTML, CSS, and JavaScript Page structuring, responsive design, and accessibility. Backend Languages: Python Python in Web development Variables, operators, control structures Best practices. Functions and Data Handling Functional programming for the web Data types Data handling and structures Code organization. Forms and User Interaction Interaction methods: POST and GET. Data validation and sanitization Data persistence Sessions and Cookies Authentication, content personalization, and security. Files and Directories Reading, writing, uploading, and organizing dynamic content. Modular Programming Use of includes and require for code reuse. Security Prevention of code injection, XSS, and CSRF. Practical Project Development of a dynamic and secure web application.
- The curricular unit Programming II aims to develop solid knowledge in web development in students, enabling them to create dynamic applications using HTML, CSS, and Python. Students will acquire skills in structuring and styling web pages, implementing interactivity, managing databases, and integrating dynamic components. In addition, they will be able to apply good programming practices, security, and optimization, ensuring the scalability and efficiency of applications. The CU also promotes problem-solving in information systems development, integratig functionality and data presentation to end users (frontend) and the development of programming logic and data access and manipulation (backend). Upon completion, students will have the aptitude to design, develop, and maintain functional, interactive, and secure websites and web applications, consolidating the link between theory and practice in a professional context.
- Mandatory
- The curricular unit of Programming II adopts innovative methodologies to optimize the teaching-learning process, combining theory and practice in dynamic approaches. It uses project-based learning, built throughout the semester and incremented with each new content, where students develop real-world web applications, promoting autonomy and problem-solving. The hands-on methodology encourages students to apply the concepts and languages addressed from the very beginning. The use of interactive classes with practical challenges stimulates active participation, while collaborative tools allow continuous review and feedback. Learning is complemented by digital resources and tools widely used in the industry (e.g., GitHub), encouraging engagement, collaborative work, and consolidating knowledge. These strategies ensure teaching aligned with market demands, preparing students for real challenges in web development.
- Português
- Flanagan, D. (2020). JAVASCRIPT : the Definitive Guide. O'Reilly Media, Inc. (ISBN: 978-1491952023) Zelle, J. (2024). Python Programming: An Introduction to Computer Science, 4th Edition. Franklin, Beedle & Associates (ISBN: 978-1590282977) Vincent, W. (2024). Django for Beginners, 5th Edition. Still River Press (ISBN: 978-1735467269) Mele, A. (2024). Django 5 By Example, 5th Edition. Packt Publishing (ISBN: 978-1805125457)
- 4
- 0
- 5
- 1
- IPLUSO6030-13151
- Programming II
- 13151
- 6030
- Information Systems Management
- Details
- Category: Discipline
- Não
- The subject of Quantitative Methods is part of the field of action of mathematics and statistics. The aim of this discipline is to provide participants with methods and tools that help them in daily operations related to mathematical reasoning and tasks that require quantitative skills, research, analysis and data presentation.
- Semestral
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 1
A acordar com os alunos 50%
Teste de avaliação 2
A acordar com os alunos 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...
- Concept Review Introduction / The data Data presentation: tables and graphs Frequency distribution Location Measures Dispersion and Concentration Measures Asymmetry and Kurtosis Measures Index numbers Association between two variables / Linear Regression and Simple Correlation Time series Application of statistical methods to the management of I.S. Study of statistics in digital support Statistical tests
- Deepen the ability to perform intellectual activities that involve mathematical reasoning; Learn to use mathematics and statistics in understanding situations in reality; Develop a critical sense regarding mathematical and statistical procedures and results;
- Mandatory
- Case study method Field investigation work Practical exercises in class Use of questionnaire and data analysis technologies such as SPSS, Typeform, Google Forms and Microsoft Excel
- Português
- Reis, Elizabeth. (2008). Estatística Descritiva, 7a Ed., Lisboa: Edições Sílado; Barroso, M., Sampaio, E. e Ramos, M. (2010). Exercícios de Estatística Descritiva para as Ciências Sociais. Lisboa: Edições Sílabo
- 4
- 0
- 5
- 1
- IPLUSO6030-4944
- Quantitative Methods
- 4944
- 6030
- Information Systems Management
- Details
- Category: Discipline
- Não
- 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.
- Semestral
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% - 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
- 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.
- Mandatory
- 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
- Português
- 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
- 4
- 0
- 5
- 2
- IPLUSO6030-15430
- Data Mining
- 15430
- 6030
- Information Systems Management
- Details
- Category: Discipline
- Não
- 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.
- Semestral
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.
- 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.
- 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.
- Mandatory
- 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).
- Português
- 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.
- 4
- 0
- 5
- 2
- IPLUSO6030-523
- Human Resources Management
- 523
- 6030
- Information Systems Management
- Details
- Category: Discipline
- Não
- This UC aims to provide the necessary skills for understanding concepts and modeling investment projects in business, with regard to techniques for their construction, evaluation criteria, risk analysis and conclusions.
- Semestral
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
05-12-2024
45%
Portfolio
9-01-2024
Data limite
45%
Participação, atitudes e assiduidade(...)
10%
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...
- 1. Introduction to Investment analysis: Concepts of; Investment, Project, Profitability, Cash Flow, Present Value Calculations: Interest rate and Discount cash flow 2. The Business Plan and its importance for the analysis and evaluation of the investment. 3. Investment analysis and evaluation 4. Sensitivity analysis 5. Conclusion and project evaluation report
- This CU aims to develop the student knowledge, skills and competences that are listed: 1. Know how to explain concepts of investment analysis; 2. Know how to make a business plan; 3. Describe a business model; 4. Describe sources of financing; 5. Know how to analyses the economic and financial situation of a company; 6. Know how to evaluate investment projects.
- Mandatory
- Expositional Methodology and Self-study Methodology: Knowledge acquisition will be measured through a test. Participative Methodology and Active Methodology: Skill development will be assessed through team work, and consists of a presentation and discussion of a case study
- Português
- Duarte, C. e Esperança, J.P. (2014). Empreendedorismo e Planeamento Financeiro. 2ª edição. Edições Sílabo. Menezes, H.C. (1999). Princípios de Gestão Financeira. 7ª edição, Lisboa, Editorial Presença, Mota, A. G. & Custódio, C. (2012). Finanças da Empresa. 7.ª Edição, Bnomics Soares, J.O., Fernandes, A.V., Março, A.A. e Marques, J.P.P. (1999). Avaliação de Projectos de Investimento na Óptica Empresarial. Edições sílabo. Soares, I., Moreira, J., Pinho, C. e Couto, J. (2020). Decisões de Investimento – Análise Financeira de Projectos. 4ª edição. Rdições Sílabo.
- 4
- 0
- 5
- 2
- IPLUSO6030-18085
- Project and Investment Analysis
- 18085
- 6030
- Information Systems Management
