- Details
- Category: Discipline
- Abbott, D. (2023). Everyday Data Visualization: A field guide for design techniques that will improve the charts, reports, and data dashboards you build every day. Manning Publications. Fleming, Clayton (2024). ASP.NET Core: A Beginner’s Guide to Efficient Web APIs with ASP.NET Core. Independently published (Amazon) Freeman, A. (2024). Mastering Node.js Web Development (1.ª ed.). Packt Publishing. Mastering¿Node.js¿Web¿Development Hajian, Majid (2019). Progressive Web Apps with Angular, Apress. Portela, Filipe e Queirós, Ricardo (2018). Introdução ao Desenvolvimento Moderno para a Web - do Front-end ao Backend: uma visão global, FCA. Shute, Zachary (2019). Advanced Javascript, Packt Publishing. Subramanian, H., & Raj, P. (2019). Hands-On RESTful API Design Patterns and Best Practices: Design, develop, and deploy highly adaptable, scalable, and secure RESTful web APIs. Packt Publishing.
- Web Systems and Technology Engineering
- 6159
- 22493
- Advanced Web Programming
- ISLA Santarém6159-22493
- 1
- 6
- 0
- 25
- Não
- Português
- The teaching methodology uses the exposure of each topic of the contents, then the practical application through exercises and works, since this curricular unit is essentially laboratory practice using mainly the methodology Problem Based Learning (PBL).
- Mandatory
- Provide students with the knowledge that allows them to program dynamic web pages, using different languages and technologies, both on the client side and on the server side, with databases, integrated and secure. Using the most emerging methodologies and frameworks. The web server language, PHP and ASP.NET; Construction of web systems, with component frontoffice and backoffice; - Construction of dashboards. At the end of CU students should be able to: Build dashboards with data visualization; Design Web applications, in PHP, ASP.NET C #, with access to databases; Dominate and apply the MVC Methodology; Build distributed applications, modular and with integration components; - Apply security policies and techniques.
- 1. Web Development Models 1.1 AMP – Accelerated Mobile Pages; 1.2 SPA – Single Page Application 1.3 PWA – Progressive Web Application 1.4 UWD – Universal Web Development 2. Advanced JavaScript 2.2 Server and client technologies: React, Angular, and NodeJS 2.3 XML and JSON 2.5 Canvas 2.6 Study of emerging frameworks 3. Data visualization on the Web 3.1 Dashboards 3.2 Visualization Techniques and Algorithms 3.3 Systems and applications: Google Data Studio, Google Charts, Flourish Studio, D3js, HighCharts, ChartsJS, Fusion Charts, Qlik Sense, Canvasjs 4.PHP 4.1 Procedural, Object-Oriented, and MVC Methodology 4.2 Data Access 4.3 Templates 4.4 Security 4.5 PHP Frameworks 5. ASP.NET 5.1 Procedural, Object-Oriented, and MVC Methodologies 5.2 Data Access 4.4 Master Pages 4.5 Security 6. Integration Technologies 6.1 XML, JSON 6.2 Web Services, REST 6.3 APIs 6.4 AJAX 6.5 Azure, AWS, Google Cloud 7. Web Security 7.1 Methodologies and Best Practices 7.2 OWASP
Avaliação contínua:
- Trabalho prático (Relatório (20%) e projeto (80%);
Avaliação final:
Todos os estudantes que não tenham concluído com sucesso a avaliação podem realizar um exame final prático (100%) na época de avaliação definida pela instituição.
- Semestral
- This course covers dynamic programming with PHP, ASP.NET, MVC, dashboards, and data visualization via D3.js and HighCharts. It includes web security (OWASP), REST APIs, and technologies such as React and NodeJS for SPA/PWA apps. Designed for intensive practice, it trains full-stack programmers capable of integrated and secure solutions.
- Details
- Category: Discipline
- Branco, A. (2011). Manutenção, Instalação e Reparação de Computadores. FCA, Editora de Informática. Delgado, José & Ribeiro, Carlos (2011). Arquitetura de Computadores. FCA, Editora de Informática. John Hennessey, John L. Hennessy, David Goldberg (2011). Computer Architecture: A Quantitative Approach, Morgan Kaufman. Null, Linda (2010). Princípios Básicos de Arquitetura e Organização de Computadores, Bookman.
- Information Systems Technology and Programming
- 2045
- 15493
- Computer Hardware
- ISLA Santarém2045-15493
- 1
- 5
- 0
- 25
- Não
- Português
- x
- Mandatory
- x
- x
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
dd-mm-yyyy
50%
Portfolio
dd-mm-yyyy
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
- x
- Details
- Category: Discipline
- Damas, L. (2017). SQL - Structured Query Language. Lisboa: FCA Gouveia, F. (2021). Fundamentos de Bases de Dados. Lisboa: FCA. Meier, A., & Kaufmann, M. (2019). SQL & NoSQL databases. Springer Fachmedien Wiesbaden. Ramakrishnan, R., Gehrke, G. (2018). Database management systems. 3rd edition. New York: McGraw-Hill. Silberschatz, A., Korth, H. F. & Sudarshan, S. (2019). Databse System Concept. McGrawHill.
- Data and Technology Management in Health
- 7055
- 26683
- Fundamentals of Data Management
- ISLA Santarém7055-26683
- 1
- 5
- 0
- 25
- Não
- Português
- Distance learning: 1. Theoretical classes presenting the subject matter using the expository method, followed by the application of interrogative and interactive methods for immediate consolidation of knowledge. Face-to-face: 2. Use of the Problem-Based Learning (PBL) methodology, where each of the topics studied will follow the following phases: a) identification and definition of the problem to be solved. b) Access and use of relevant information to solve the problem. c) Problem solving. d) Presentation of results and critical analysis. Independent: 3. Solving additional exercises proposed by the teacher. The teacher provides feedback (Tutorial Guidance - TG) on the results obtained by the student in solving the proposed problems, either in person in the classroom or remotely in asynchronous mode via the Moodle platform.
- Mandatory
- O1. Provide students with knowledge of fundamental concepts, methods, and techniques in the field of databases. O2. Build relational data models appropriate to the requirements of an information system, using conceptual and logical database design techniques. O3 Explore the potential of a database management system (DBMS). O4 Explore NoSQL databases. Skills: C1. Design database systems suited to the needs and objectives of organizational management. C2. Create databases by implementing the mechanisms necessary for data integrity. C3. Properly use information extraction and database query operations. C4. Use database management mechanisms, taking into account operational issues (security, concurrency).
- 1. Basic concepts of databases: File System vs. DBMS. Database management systems. Database models. Transactions. Performance and scalability. Data access and security. 2. Design of relational databases: Entity-relationship model. Normalization theory. 3. Relational algebra: SQL - Structured Query Language. Data Manipulation Language (DML) statements. Data Definition Language (DDL) statements. View creation and manipulation. Index creation and manipulation. 4. Database query languages: T-SQL and PL-SQL. 5. NoSQL databases: ACID and BASE. Design and implementation. 6. Database applications. 7. Database development tools.
Avaliação Curricular (contínua):
A1. Projeto prático (relatório e projeto). A2. Teste final individual.
A classificação final é calculada através da fórmula Classificação Final = 0,5*A1+0,5*A2. O estudante é aprovado se obtiver classificação igual ou superior a 9,5 valores.
Avaliação Final: O estudante realiza o exame completo (A=100%) e é aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.
Avaliação em Época de Recurso e Época Especial (A): O estudante realiza o exame completo (A=100%) e fica aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.
- Semestral
- Details
- Category: Discipline
- Marketing
- 7153
- 23
- English
- ISLA Santarém7153-23
- 1
- 5
- 0
- 25
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Aggarwal, C. C. (2021). Artificial Intelligence A Textbook. Springer. Chopra, D., & Khurana, R. (2023). Introduction to Machine Learning with Python. Bentham Science Publishers. Miller, B. N., & Ranum, D. L. (2023). Problem solving with algorithms and data structures using Python (4th ed.). Franklin, Beedle & Associates. Russell, S., & Norvig, P. (2021). Artificial intelligence: a modern approach. Pearson. Teoh, T. T., & Rong, Z. (2022). Artificial Intelligence with Python. Springer Singapore.
- Web Systems and Technology Engineering
- 6159
- 2129
- Artificial Intelligence
- ISLA Santarém6159-2129
- 1
- 6
- 0
- 25
- Não
- Português
- The teaching methodology involves the exposure of each topic of content, with practical application immediately through exercises and work, since this course is essentially laboratory practice. Therefore, the Problem Based Learning (ABRP) methodology will be used.
- Mandatory
- Study the main areas of Artificial Intelligence: Intelligent agents, Search, Problem-solving methods, Heuristics and meta-heuristics, Knowledge Representation and Reasoning, and Machine Learning. Skills: Identify problems that can be solved with Artificial Intelligence; Represent knowledge with computational structures; Programming in logic; Understand and apply the main problem-solving algorithms automatically; Apply Machine Learning techniques; Implement the main algorithms in C#; Use Python AI libraries.
- 1. Introduction to Artificial Intelligence and its applications 2. Intelligent agents and logical agents 3. Knowledge representation, reasoning, and logic 3.1 Structures and objects 3.2 Knowledge-based agents 3.3 Representation, reasoning, and logic 3.4 Transforming Knowledge into Action 3.5 Propositional, Predicate, Modal, and Temporal Logic 3.6 Introduction to Logic Programming 4. Problem-Solving Methods 4.1 Search Agents 4.2 Problem Formulation 4.3 Informed and Uninformed Search 4.4 Evolutionary Computation 4.5 Constraint Satisfaction Problems 4.6 Problems Considering Adversaries 4.7 Modern Heuristics 5. Machine Learning Classification and Categorization 5.1 Inductive Learning 5.2 Neural Networks 5.3 Data Science 5.4 Deep Learning 6. Implementation of Algorithms Implementation in C# AI Libraries in Python
Avaliação contínua:
- Trabalho prático (Relatório e projeto); 60%;
- Teste final prático; 40%.
Avaliação Final:
Todos os estudantes que não tenham concluído com sucesso a avaliação continua podem realizar um exame final teórico-prático (100%) na época de avaliação definida pela instituição.
- Semestral
- This course explores advanced techniques such as intelligent agents, search algorithms, machine learning, and neural networks. Students apply AI models in real-world scenarios, developing decision support systems with open-source tools. With an emphasis on theoretical and practical classes, it promotes skills for innovation in automation and predictive analytics.
