Structured Programming

Category: Discipline
  • Não
  • The Structured Programming course aims to provide students with the foundations of structured/Imperative programming, developing and applying concepts of programming logic, algorithms and data structures, through the use of the Python programming language. In this UC, the development of applications in Python, its application in data science, as well as its interaction with other systems/programming languages, such as databases and spreadsheets, are also explored.
  • 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

    13-01-2026

    30%

    Analise de um  artigo académico no âmbito do desenvolvimento de  aplicações para ciência de dados (apresentações)

    09-12-2025

    40%

    Trabalho de projecto (apresentações)

    20-01-2026

    30%

     

    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...

     
  • CP1 - General programming topics: high and low level languages, algorithms, interpreters and compilers CP2 - Notions of Programming Logic: Imperative/structured programming CP3 - Data Structure (Lists, Dictionaries, Sets, Tuples, Matrices, General Types (float, integer, string,..) CP4 - Conditional Structures CP5 - Repetition Structures CP6 - Interaction with the user and main program CP7 - Functions and libraries, and their applicability in data science CP8 - Interaction with other programming languages/software/systems: sql databases and Excel spreadsheets
  • OA1 - Introduction to computer programming: programming languages ¿¿(high and low level) paradigms, interpreters.  OA2 - Introduction to programming logic, algorithms and data structure OA3 - Data Structure in Python: general types (integer, float, boolean, ..), Lists, Dictionaries, Sets, Tuples   OA4 - Repetition structure and conditionals in Python (while, for, if, switch, etc.) OA5 - Functions and Libraries in Python and their application in data science  OA6 - Interaction with other systems/programming languages
  • Mandatory
  • The assessment is carried out through a presentation and analysis of an academic article, within the scope of the development of applications in data science (AA), the development and presentation of group work, aiming at the development of an application in Python, applied to data science (TG) and a written test (T), with a view to evaluating the knowledge developed here. In this way, the Final Grade obtained (NF) results from the application of the following formula: . NF = 0.30*AA+0.30*TG+ 0.40*T Where: AA - Presentation and analysis of an academic article within the scope of the development of applications for data science TG - Development and presentation of group work, aiming to develop an application in Python T – Written test, with a view to evaluating the knowledge developed here Practical exercises will also be developed throughout the classes
  • Português
  • •Behrman, K., Brodbeck, H., (2022) Fundamentos de Python para Ciência de Dados, Bookman •Costa, E.(2015) Programação em Python Fundamentos e Resolução de Problemas, FCA •Behrman, K. (2022) Foundational Python For Data Science, Pearson Education •Forbellone, A.L.V., Eberspächer, H.F.,(2022) Logica de Programação, 4ªedicao, Bookman Apontamentos e slides fornecidos pelo docente  
  • 4
  • 0
  • 4
  • 1
  • IPLUSO6382-22254
  • Structured Programming
  • 22254
  • 6382
  • Computer Applications for Data Science

Data Center Technology

Category: Discipline
  • Não
  • The Datacenter Technology course aims to equip students to understand the technologies used in data centers, in order to plan, implement, manage, and maintain these critical IT environments. With a theoretical and practical approach, the course explores everything from the basic concepts of data centers to advanced topics such as high availability, security, automation, and cloud computing.   The scope of Datacenter Technology is broad, as the use of data centers is increasing in companies and organizations that rely on technology to operate. The relevance of this course in the study cycle is essential, especially in courses related to Data Science, as the knowledge acquired is critical for the implementation and management of IT infrastructures necessary for data analysis
  • Semestral

  • A presente Unidade Curricular apresenta duas metodologias de avaliação: Avaliação Contínua e Avaliação Não Contínua.

    1. Avaliação Contínua:

    A Avaliação Contínua desta Unidade Curricular é composta por um teste (T) e 2 trabalhos práticos (TP1 e TP2). A nota final (NF) é calculada como sendo, a média aritmética dos elementos de avaliação de acordo com  a seguinte fórmula:

    (NF = 0.4 * T + 0.40 * TP1+0.20 * TP2)

     

    2. Avaliação Não Contínua:

    Os alunos podem optar por exame, sendo a classificação final igual à deste elemento de avaliação.

    A UC, é composta por 3 elementos de avaliação:

    T - Teste de avaliação

    TP1 - Trabalho Prático Nº 1 - Projecto de um Data Center 

    TP2 - Trabalho Prático Nº 2 - Análise de um artigo acadêmico no âmbito de Data Centers

     

    Datas de Avaliação:

    Teste(T):  18/06/2026

    Trabalho Pratico 1 (TP1): 24/06/2026 

    Trabalho Pratico 2 (TP2): 22/05/2026

     

     

     
  • 1: Introduction to Datacenters Basic concepts and definitions Types of datacenters and their characteristics 2: Datacenter Infrastructure Servers: types, components and configurations Network: switches, routers, protocols and topologies Storage: disks, SAN, NAS and backup systems 3: Cloud Computing and Virtualization Concepts Features, services and implementations Challenges and benefits 4: Planning and Design of a Datacenter Site selection and preparation Power and cooling requirements 5: Implementation and management of a Datacenter Installation and configuration of servers, networks, storage and virtualization Automation and Monitoring 6: Security and High Availability Physical and logical security measures disaster recovery Load balancing and clustering 7: Emerging Technologies and Trends Artificial intelligence Green datacenters and sustainability 8: Project and Test Preparation
  • Understand the basic concepts of data centers and the technologies involved, such as servers, networks, storage, and virtualization. Identify and apply best practices for planning, implementing, managing, and maintaining a data center. Develop practical skills for monitoring, troubleshooting, and optimizing the performance of a data center. Understand the techniques and tools used to ensure the security and high availability of a data center. Familiarize with emerging technologies such as cloud and automation, and understand how they can be applied in data centers to improve efficiency and reduce costs.
  • Mandatory
  • The course adopts a theoretical and practical approach, combining the teaching of fundamental data center concepts with hands-on application in topics such as high availability, security, automation, and cloud computing. Students are challenged to apply the knowledge acquired in the implementation and management of critical IT infrastructures, promoting the consolidation of learning through targeted exercises and practical case studies.
  • Português
  • Nikolov, I. M., & Maloo, S. (2022). Data Center Fundamentals. Pearson Education. Hwang, K., & Dongarra, J. (2012). Distributed and Cloud Computing: From Parallel Processing to the Internet of Things. Morgan Kaufmann. Sebenta/apontamentos cedidos pelo docente.  
  • 4
  • 0
  • 6
  • 1
  • IPLUSO6382-23553
  • Data Center Technology
  • 23553
  • 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

Statistics for Data Science

Category: Discipline
  • Não
  • This course introduces the fundamental concepts of statistics for data science, including probability distributions, sampling techniques, statistical inference, exploratory data analysis and curve fitting. In addition, the course introduces statistical modeling techniques, including linear regression and analysis of variance, as well as multivariate analysis techniques, such as principal component analysis and clustering.
  • 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

    Primeira Frequência

    30-04-2025

    40%

    Segunda Frequência

    11-06-2025

    40%

    Apresentação de trabalho Fração A

    		              07-05-2025

    25%
                                            Apresentação de trabalho Fração B              16-06-2025                   25%
                                                           Participação                     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 statistics and probability 2. Sampling techniques 3. Statistical inference 4. Exploratory data analysis 5. Statistical decision theory, hypothesis tests and significance tests. 6. Theory of small samples. Student's t distribution and Chi square distribution. 7. Curve fitting and the least squares method 8. Statistical modeling 9. Multivariate analysis
  • 1. Understand the fundamental concepts of probability and probability distributions; 2. Apply sampling techniques to collect and analyze data; 3. Perform statistical inference, including hypothesis testing and confidence intervals; 4. Perform exploratory data analysis using graphs and descriptive statistical measures; 5. Apply statistical modeling techniques, including linear regression and analysis of variance; 6. Apply multivariate analysis techniques, such as principal component analysis and clustering.
  • Mandatory
  • The teaching methodology will be based on lectures, practical examples and exercises, as well as the use of data analysis software (e.g. R commander or Jamovi). Students will also be encouraged to develop practical projects involving the analysis of real data, with a focus on interpreting and communicating the results. Assessment will be based on individual and group work, as well as two tests. Students will also be assessed on their ability to apply statistical concepts to real problems and their ability to communicate data analysis results clearly and concisely with appropriate technical terminology.
  • Português
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R. Springer. Steele, B., Chandler, J., & Reddy, S. (2018). Statistics for data science: A comprehensive introduction. O'Reilly Media, Inc. Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
  • 4
  • 0
  • 6
  • 1
  • IPLUSO6382-23558
  • Statistics for Data Science
  • 23558
  • 6382
  • Computer Applications for Data Science
  1. Advanced Data Science
  2. Analysis and Treatment of Multivariate Data
  3. Data Warehouse and Business Intelligence
  4. Innovation and Entrepreneurship

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