Server Operating Systems - Linux Platform

Category: Discipline
  • Não
  • The scope of this curricular unit is to introduce students to operating systems based on the Linux kernel. The course program as well as its teaching methodology revolves around the ability of students to sufficiently understand the functioning of these operating systems and some services that work on these operating systems.
  • 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 prático

    29-10-2025

    50%

    Trabalho prático

    7-01-2026

    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...

     
  • Installation of Linux operating systems with and without a graphical environment Debian and Rocky Installation Adding users as system administrators SSH service Installation configuration SSH access to servers DHCP Service Installation and configuration IP Pool Definition IP reservation/assignment by MacAddress DHCP with different networks Placing DHCP clients on different networks DNS Service Installation, configuration and operation Direct and reverse zone Most used RRs, A, AAAA, PTR, SOA, NS, CNAME, MX HTTP Service (Apache and Nginx) Installation and configuration (Apache and Nginx) Virtualhosts in Apache and Nginx User authentication on virtualhosts Virtualhosts working on SSL/TLS Syslog Service SyslogServer /var/log folder
  • Provide students with knowledge of the functioning of various operating systems, their structure, management, maintenance and configuration mechanisms. Allow students to have contact with an operating system other than WindowsTM. Provide basic knowledge of maintaining UnixTM-like systems in the absence of graphical environments. Take students to configure and manage network management services, computer systems, users, installed on Linux systems. Encourage students to acquire their own independence in terms of problem-solving learning, indicating objectives to be achieved, without presenting a final solution, indicating generic information presented in the course as a basis, and teaching them to search the Internet for information that allows them to solve these more specific problems
  • Mandatory
  • Classes will be in person, with demonstrations by the teacher of all steps: installation, configuration of Linux servers; installation and configuration of services; operation and error detection of services. During this demonstration, the theory inherent to the subject being taught will be addressed. The demonstration of the different steps, which involve installation, configuration, operation, detection and correction of errors, will be carried out for periods of 15/20 minutes, by the teacher. With recording of these contents. These videos will be made available to students during class on a digital platform, as well as the files resulting from the tasks developed. During the same class, the student is asked to develop what was demonstrated. There is room for the student to develop the tasks presented during the class, in a later period and be sent back as an assignment. In this way, learning will be progressive and based on knowledge acquired throughout the course.
  • Português
  • Granjal, Jorge - GESTÃO DE SISTEMAS E REDES EM LINUX 3ª Edição Atualizada, FCA Editora de Informática (Coleção: Tecnologias de Informação), Lisboa, 2010, ISBN: 978-972-722-784-6  
  • 4
  • 0
  • 6
  • 2
  • IPLUSO6130-22249
  • Server Operating Systems - Linux Platform
  • 22249
  • 6130
  • Cybersecurity

Object Oriented Programming

Category: Discipline
  • Não
  • The Course Unit of Object-Oriented Programming (OOP) aims to provide students with solid knowledge of the programming paradigm that underpins most current programming languages and software development technologies. Its scope lies in introducing and deepening the fundamental concepts of object orientation, fostering the ability to apply these principles in the development of robust, scalable, and reusable applications. The unit also seeks to stimulate logical and abstract reasoning skills, preparing students to solve complex problems in diverse contexts through the practical application of modeling and implementation techniques in Java. The relevance of this unit stems from the fact that OOP constitutes the essential conceptual and practical foundation for progression into more advanced areas of software engineering and development.
  • Semestral

  • Descrição

    Data limite

    Ponderação

    Entrega, Apresentação e Defesa do Projeto

    08-01-2026

    65%

    Entrega do Portefólio Individual

    08-01-2026

    35%

    Exame de Recurso

    -

    100%

    A avaliação contínua inclui a realização, individualmente ou em pequenos grupos, de um projeto que materializa a aplicação dos conceitos da POO, representando 65% da nota final, e a elaboração de um portefólio individual, no qual cada estudante reflete de forma crítica sobre as aprendizagens realizadas, documenta exercícios desenvolvidos e avalia a sua evolução ao longo do semestre, correspondendo a 35% da nota final. Esta distribuição assegura um equilíbrio entre o desempenho técnico e a capacidade reflexiva, garantindo uma avaliação integral e formativa do processo de aprendizagem. Em alternativa à avaliação contínua, os estudantes que não obtenham aprovação podem realizar um exame escrito de recurso, com um peso de 100% da nota final.
  • Introduction to the Object-Oriented Programming (OOP) paradigm What is OOP and Benefits of OOP Concepts used in OOP (methods, fields, classes, objects, etc.) Objects and classes in OOP; Definition of classes Creation of objects; Constructors Inheritance What inheritance is in OOP Extension of classes Method overriding through the superclass Polymorphism; Method overloading Dynamic methods in OOP Encapsulation Access modifiers Getters and setters in OOP Abstraction Abstract classes; Interfaces Relevant topics in OOP Exceptions and their handling File systems (I/O) Inner classes and static members Structures and code organization Organization and structure of code and their importance
  • At the end of the course unit, the student should be able to: Understand the fundamental principles of Object-Oriented Programming. Define classes, objects, and methods in Java, correctly applying constructors. Apply the concepts of encapsulation, inheritance, polymorphism, and abstraction to concrete problems. Develop solutions using interfaces, abstract classes, and method overloading and overriding mechanisms. Implement exception handling, input/output operations, and modular code organization. Work collaboratively in a team in the development of software solutions. Present and defend developed solutions, providing well-founded arguments. Critically reflect on the process of learning and software development, producing appropriate technical documentation.
  • Mandatory
  • The course unit adopts active teaching and learning methodologies, privileging the articulation between theory and practice. Project-Based Learning (PBL) constitutes the structuring axis of the process, promoting autonomy and the resolution of real problems, which ensures greater motivation and engagement. In addition, the completion of practical exercises and the resolution of cases in class, individually and/or collaboratively, allows for the immediate application of the concepts presented. Collaborative work and peer assessment foster discussion, critical reflection, and the sharing of different perspectives, developing social and communication skills that are fundamental in professional contexts. Throughout the entire process, formative assessment, with continuous feedback from the lecturer, ensures that students can identify and overcome difficulties, progressively consolidating their knowledge and competences.
  • Português
  • Schildt, H. (2022). Java: The Complete Reference. McGraw-Hill Education Coelho, P. (2016). Programação em Java - Curso Completo . 5a Edição Atualizada. FCA.ISBN 9789727228409  
  • 4
  • 0
  • 6
  • 2
  • IPLUSO6130-16225
  • Object Oriented Programming
  • 16225
  • 6130
  • Cybersecurity

Analysis and Treatment of Multivariate Data

Category: Discipline
  • Não
  • 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.
  • Semestral

  • Descrição

    Data limite

    Ponderação

    Teste de avaliação

    17-11-2025

    30%

    Projeto Final

    19-01-2026

    70%
         

     

     

     
  • 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.
  • 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.
  • Mandatory
  • 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.
  • Português
  • 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.  
  • 4
  • 0
  • 5
  • 2
  • IPLUSO6382-23559
  • Analysis and Treatment of Multivariate Data
  • 23559
  • 6382
  • Computer Applications for Data Science

Data Communication Bases

Category: Discipline
  • Não
  • Databases are at the heart of modern commercial application development. In addition, their use extends to many other environments and domains where large amounts of data must be stored for efficient update, retrieval, and analysis. This course provides an introduction to fundamental principles, methodologies for effective database design, and to the SQL language. The course will be driven by a set of practice activities, conducted along the semester, that will allow the students to acquire the required skills.
  • Semestral

  •  

    AVALIAÇÃO

    Descrição

    Data limite

    Ponderação

    Trabalhos práticos e participação nas aulas

    Final das aulas do semestre

    60%

    Testes de avaliação

    Final das aulas do semestre

    40%

    Exames (prático + teórico)

    Final da época de exames

    100%

     

     
  • 1. Introduction to Databases and DBMS. What are DB and why are they essential. Difference between structured vs. unstructured data. Traditional file systems vs. database management systems (DBMS).. Overview of database types  (Relacional, NoSQL). Introduction to Database Management Systems (DBMS). 2. Relational Model & Database Design Data models. Tables, Rows and Columns. Constraints, Primary Keys, Foreign Keys. Relationships in a Database. Introduction to Entity-Relationship (ER) Diagrams. Normalization concepts. 3. SQL Language What it is and why it's used. Data Definition (CREATE, ALTER, DROP).  Data Manipulation (INSERT, UPDATE, DELETE). Basic Queries: (SELECT, WHERE, ORDER BY). Advanced Queries (JOIN, GROUP BY, aggregate funciotns, sub-queries and nested queries) Concurrency (COMMIT e ROLLBACK) Data Integrity (Constraints) Security (GRANT e REVOKE) 5. Database Backup and Recovery Strategies. Backups (Physical and Logical) and Data Recovery.
  • At the end of this course, students will be able to: 1. Understand DBMS systems architecture and components 2. Undestand the relational model approach to data management. 3. Model Entity-Relationship diagrams for a database 4. Formulate queries using the SQL Language 5. Apply different normal forms to design a database 6. Identify suitable Indices for effective storage and retrieval of data. 7. Undestand how access control is performed in a DBMS.  
  • Mandatory
  • Use of problem-based learning methodology, w hich allows the student to acquire knowledge, at the same time that carrying out the set of procedures for solving problems allows them to develop skills and competences. This methodology promotes learning as part of the activity developed to solve the problem.
  • Português
  • Garcia-Molina, H., Jeffrey David Ullman, & Widom, J. (2014). Database systems: the complete book. Pearson, Cop. Damas, Luís – SQL - Structured Query Language, 14ª Edição atualizada (2020). FCA (2020). ISBN13: 978-972-722-829-4    
  • 4
  • 0
  • 4
  • 1
  • IPLUSO6382-23552
  • Data Communication Bases
  • 23552
  • 6382
  • Computer Applications for Data Science

Discrete Mathematics

Category: Discipline
  • Não
  • The course of Discrete Mathematics aims to contribute to the acquisition of a set of skills in students: the ability to assimilate information and to communicate it; the ability of written expression; the ability of oral expression and the ability of mathematical argument. To this end, concepts of mathematical logic, set theory and number theory will be studied.
  • Semestral

  • A avaliação da unidade curricular pode ser realizada em avaliação continua sendo esta composta por duas frequências com o peso de 40% cada, por um trabalho com o peso de 15% e de 5% avaliado ao longo das aulas compreendendo o comportamento, a participação e a assiduidade, ou em exame final com um peso de 100%.

    Descrição Datas Ponderação
    1ª Frequência 20-04-2026 40%
    2ª Frequência 08-06-2026 40%
    Trabalho Prático 21-06-2026 15%
    Aula ao longo do semestre 5%

     
  • S1. Logical Preliminares S2. Set theory S3. Functions S4. Notions of the sets' cardinality  S5. Number theory
  • The main objectives of this unit are: LO1. Apply the elementary properties of the logical and quantification operations; LO2. Developing mathematical arguments using the usual methods of proof; LO3. Explain the basic concepts of sets and functions and perform elementary operation between these mathematical objects; LO4. Distinguish between countable and non-countable sets; LO5. Understand the number theory; LO6. Contribute for the acquisitoon of a set of skills: ability to assimilate and communicate information, ability to express themselves in writing; ability to express themselves orally.  
  • Mandatory
  • The teaching methodology includes the expository method (TM1) to present the contents, the demonstrative method (TM2) to illustrate its application to practical cases and the active method (TM3) to solve classroom exercises. The assessment is made by continuous assessment or written exam.
  • Português
  • Slides e apontamentos das aulas  
  • 4
  • 0
  • 4
  • 1
  • IPLUSO6382-1656
  • Discrete Mathematics
  • 1656
  • 6382
  • Computer Applications for Data Science
  1. Statistics for Data Science
  2. Advanced Data Science
  3. Innovation and Entrepreneurship
  4. Storage for Big Data

Page 106 of 225

English Menu

Pesquisa

Login Form