- Details
- Category: Discipline
- •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
- Computer Applications for Data Science
- 6382
- 22254
- Structured Programming
- IPLUSO6382-22254
- 1
- 4
- 0
- 4
- Não
- Português
- 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
- Mandatory
- 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
- 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
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...
- Semestral
- 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.
- Details
- Category: Discipline
- Livros Zelle, J. M. (2024). Python Programming: An Introduction to Computer Science (4th ed.). Franklin, Beedle & Associates Inc. Matthes, E. (2023) Python Crash Course: A Hands-on, Project-Based Introduction to Programming (3rd ed.). No Strach Press
- Information Systems Management
- 6030
- 13147
- Programming I
- IPLUSO6030-13147
- 1
- 5
- 0
- 4
- Não
- Português
- The Curricular Unit employs innovative methodologies to enhance teaching and learning, including Project-Based Learning (PBL), which applies theoretical concepts through real projects. Gamification incorporates game elements to boost student engagement and motivation. Blended learning combines in-person and online classes, providing flexibility and diverse resources. Collaborative learning through group work and digital platforms develops teamwork and socio-emotional skills. Interactive tools like quizzes and simulators reinforce knowledge dynamically and personally. These methodologies create an active, student-centered environment, facilitating the acquisition of essential skills for the modern professional market.
- Mandatory
- The learning objectives of the Curricular Unit Programming I aim to provide students with a solid understanding of fundamental programming concepts, including Python language syntax, data structures, and programming paradigms. Additionally, the course seeks to develop practical skills in writing, debugging, and testing Python code, as well as using development tools. The competencies to be acquired encompass the ability to efficiently solve computational problems, algorithmic thinking, collaborative work on software projects, and adaptability to new technologies, preparing students for professional and academic challenges in the field of computer science.
- Introduction to Programming and Development Environment Variables, Data Types, and Operators Control Flow Structures Functions Data Structures File Manipulation Working with Databases Object-Oriented Programming Exception Handling Libraries and Modules Practical Projects
Elementos de Avaliação
Descrição Data Limite Ponderação Teste de Avaliação 09-01-2025 30% Avaliação teórica abrangendo todos os conteúdos abordados ao longo do semestre Projeto Prático - Parte I 23-11-2005 20% Desenvolvimento de um projeto individual ou em grupo para construção da estrutura base de uma solução para enunciado disponibilizado
- Conteúdos até aula 7
- Aula 8 para dúvidas e trabalho acompanhado em sala
Projeto Prático - Parte II 23-01-2026 20% Continuação e finalização do projeto
- Acrescenta PO-O e persitência de dados (ficheiro/base de dados).
- Trabalho a completar em aula
Apresentação e Defesa de Trabalho 23-01-2026 20% Defesa de trabalho realizado, com explicação de código e demonstração de resultados Participação e Colaboração ao longo do semestre 10% Avaliação contínua da participação nas aulas, trabalhos em grupo, tutoriais e utilização da plataforma Moodle. - Semestral
- The Curricular Unit Programming I introduces the fundamentals of programming using the Python language, encompassing programming logic, control structures, functions, and data manipulation. The scope of action includes the development of computational solutions, fostering problem-solving skills and algorithmic thinking. The area of application extends to computer engineering, data science, and automation, allowing interventions in various technological domains. The relevance of the Curricular Unit in the study cycle lies in providing the essential foundation for advanced courses, empowering students for the professional market and academic progression.
- Details
- Category: Discipline
- Chiavenato, I. (2015). Recursos Humanos: O Capital Humano das Organizações, 10ª Ed., S. Paulo: Elsevier Campus Editora Pina e Cunha, M.; Rego, A.; Cunha, R.C.; Cabral-Cardoso, C.; Marques, C.A.; Gomes, J.F.S (2010) ?Manual de Gestão de Pessoas e do Capital Humano?, 2ª Edição, Edições Sílabo
- Accounting and Management
- 3564
- 18835
- Administrative Management of Human Resources
- IPLUSO3564-18835
- 1
- 5
- 0
- 4
- Não
- Português
- Also include innovative methodologies to support the teaching-learning process used
- Mandatory
- Provide students with bases associated with the administrative management of Human Resources Acquisition and mastery of knowledge about administrative responsibilities; Knowledge of human resources legislation; Knowledge about the impact and form of formal communication within organizations
- HR technical management The role of HR and its transformation Techniques and HR Internal HR training HR administrative procedures
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
08-06-2026
50%
Trabalho de grupo
15-06-2026
40%
Participação em aula
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...
- Semestral
- The Curricular Unit of Administrative Management of Human Resources has in its programmatic content the fundamentals, theoretical and practical of Administrative Management of Human Resources. It aims to prepare students to take on Human Resources administrative functions that aim to ensure legal and operational compliance with the organization's strategic decisions.
- Details
- Category: Discipline
- 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.
- Computer Applications for Data Science
- 6382
- 23553
- Data Center Technology
- IPLUSO6382-23553
- 1
- 6
- 0
- 4
- Não
- Português
- 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.
- Mandatory
- 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.
- 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
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
- Semestral
- 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
- Details
- Category: Discipline
- Bolton, J. (Ed.). (2019). Data Warehousing Essentials. Larsen and Keller Education. Deckler, G. (2022). Learn Power BI: A comprehensive, step-by-step guide for beginners to learn real-world business intelligence, 2nd Edition (2nd ed. edition). Packt Publishing Inmon, B., & Puppini, F. (2020). The Unified Star Schema: An Agile and Resilient Approach to Data Warehouse and Analytics Design. Technics Publications. Kelly, N. (2021). Delivering Data Analytics: A Step-By-Step Guide to Driving Adoption of Business Intelligence from Planning to Launch (1st edition). Kogan Page. Kimball, R. (2008). The Data Warehouse Lifecycle Toolkit, 2nd Edition (2 edition). Wiley. Kimball, R. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition (3 edition). Wiley.
- Information Systems Management
- 6030
- 9856
- Business Intelligence
- IPLUSO6030-9856
- 1
- 5
- 0
- 4
- Não
- Português
- Use of Moodle platform to enhance skills with short quizes and short practice works.
- Mandatory
- Understand the reasons why Business Intelligence is so important for organizations What architecture components can be useful and necessary to implement a BI infrastructure Modelling Schemas for DataWareHouse (DW) Know the several Data Manipulation Processes within a DW Environment Understand and explore the potential of using a data analyse plataform
- DW Business Challenges DW Architectures Modeling the Data Warehouse ¿ Best Practices ETL Overview BI Databases Reporting Tools Build Reports
Componente Teórica
- Participação/Assiduidade, Teste final – 25%
- Teste Final (20%)
- Participação (5%)
Componente Prática
- Projetos – 75%
- 1st Work – Modeling (25%)
- 2nd Work – ETL (25%)
- 3rd Work – Reporting (25%)
- Teams of 2-3 students
Nota mínima em cada momento/elemento de avaliação - 9,5 valores em 20.
- Participação/Assiduidade, Teste final – 25%
- Semestral
- The Decision Support Systems / Business Intelligence UC aims to ensure an introduction to the theme of data analysis processes to support the areas of business management. In this sense, UC has a fundamental role in this study cycle, in terms of understanding the management and decision processes of organizations, as well as to underline how are important the data infrastructures at the service of the organizations..
