- Details
- Category: Discipline
- Não
- The course aims to deepen knowledge and skills in the field of data analysis and interpretation, especially from organizational, social and digital contexts. Content related to database structuring and management is covered, as well as the application of advanced mixed analysis methods, with qualitative and quantitative data, and the effective communication of the results obtained. It is intended that students develop the ability to design, implement, and evaluate analytical processes aimed at solving complex problems and supporting decision-making, promoting a critical, ethical and reasoned approach to the use of data.
- Semestral
Reflexão escrita, dia 12/11/2025, ponderação de 25%;
Teste de avaliação, dia 14/01/2026, ponderação de 25%;
Projeto final (de grupo), entregar e apresentar dia 21/01/2026, ponderação de 25%;
Participação ativa nas dinâmicas em sala de aula durante a UC, ponderação de 25%
- Introduction to applied data science; Ethics, privacy, and data protection (GDPR) in information collection and processing; The role of data in problem solving; Quantitative data collection through questionnaires: types of questions, response scales, common errors in question formulation, pilot testing, and instrument validation. Methods of qualitative analysis of online and offline data: coding, categorisation, identification of patterns and themes in language; Computer support for analysis using specialised software for organising and exploring data; Integration of qualitative and quantitative data for comprehensive results; Exploratory analysis of mixed data; Interpretation and communication of analytical results; Application of the methods studied to case studies in digital environments.
- Knowledge: Students are expected to acquire knowledge of the fundamental principles of data science, particularly with regard to ethical processes, data collection, organisation, analysis and interpretation from different contexts, with a view to problem solving. Skills: Students should develop skills to structure data sets, select appropriate methods of analysis, interpret results and communicate conclusions in a clear and reasoned manner, using analytical approaches appropriate to the context of the problem under study. Competencies: At the end of the course, students should be able to apply methodologies (individually or in combination) to solve specific problems, support decision-making processes based on empirical evidence, and act critically and responsibly in the use of data, respecting ethical and legal principles.
- Mandatory
- The course will be developed through theoretical and practical classes, combining the presentation of fundamental concepts with the resolution of applied exercises and the analysis of case studies, from the perspective of Service Learning (SL) in the academic community. Active learning methodologies will be promoted, encouraging student participation in the exploration of data sets, discussion of results, and critical reflection on the analytical processes used.
- Português
- Andreotta, M., Nugroho, R., Hurlstone, M. J., Boschetti, F., Farrell, S., Walker, I., & Paris, C. (2019). Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behavior research methods, 51(4), 1766-1781. https://doi.org/10.3758/s13428-019-01202-8 Morettin, P. A., & Singer, J. M. (2020). Introdução à Ciência de Dados. Fundamentos e Aplicações. https://www.ime.usp.br/~jmsinger/MAE0217/cdados2020jun03.pdf Vasconcelos, J. B., & Barão, A. (2017). Ciência dos dados nas organizações. http://hdl.handle.net/10884/1424
- 4
- 0
- 5
- 2
- IPLUSO6382-23554
- Advanced Data Science
- 23554
- 6382
- Computer Applications for Data Science
- Details
- 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
- Details
- Category: Discipline
- Não
- This curricular unit aims to provide students with the knowledge and skills necessary to analyze, design and implement state-of-the-art information systems, with a view to supporting the business processes of current companies, based on different skills, preparing them for assume management and leadership positions in the technology industry.
- 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
Apresentação, analise e discussão de um artigo académico no âmbito dos Sistemas de Informação
19-12-2025
20%
1º Teste de avaliação
12-12-2025
20%
2º Teste de avaliação
16-01-2026
20%
Trabalho de grupo 23-01-2026 40% - CP1.General concepts: Data, information and knowledge of electrotechnics/electronics CP2. Typology and functions of information systems: OLAP and OLTP systems CP3. Planning, designing and modeling in information systems: UML language CP4. Database Management System (DBMS) CP5. Decision Support Information Systems in a business and industrial context: Business Intelligence ERP- Enterprise Resource Planning SCM - Supply Chain Management CRM- Customer Relationship Management Big Data & Analytics CP6. Data Knowledge Extraction Systems: ETL - Extract, Transform & Load Data Wharehouses Data Mining KDD ¿CP7: Security and ethics in information systems
- LO1: Explain the main concepts and types of information systems used by organizations; LO2: Make known the context of Information Systems and their role in the management of organizations, particularly in the field of decision-making support and business analysis; LO3:Develop the ability to design information systems, including relational database systems to support decision making in a business context; LO4: Demonstrate the ability to integrate information from the different organizational functions of a company, enhancing control and support in decision-making processes; LO5: Analyze and use information and knowledge, extracted from the information systems used by organizations, using for this purpose the most appropriate information system for each type of situation; LO6: Know, develop and appropriately use applications with a data access component, such as dashboards and other applications to support and analyze decision making;
- Mandatory
- Theoretical-Practical teaching methodology, with interactive classes designed to introduce theoretical concepts and conduct exercises or system demonstrations. Practical classes aim to prioritize student autonomy and group work. Learning objectives (LO) (1)-(5) are assessed in two partial tests, or one exam, whose grade will be the theoretical component (TC) of the assessment. The final grade (CF) for each student will result from the application of the following formula: CF=TC*0.4+CP*0.6. The Practical Component (CP) grade is obtained by the following formula: CP=CP1*0.4+CP2*0.2. Where: CP1: Group Work CP2: Analysis and Discussion of an Academic Article To be approved for the course, the student must obtain a minimum grade of 10 in both the CP and CT.
- Português
- Frye, Curtis - Microsoft Excel 2016 Passo a Passo. Edição em Português. Brasil. Bookman, 2016. ISBN 9788582603956. Rascão, José - Sistemas de Informação para as Organizações. Edições Sílabo. 2004. ISBN: 9789726183303. Lopez, Yanai - Sistemas de informação para a Gestão. Escolar Editora. 2013. ISBN: 9789725923740. Slides/Folhas de apoio disponibilizadas pelo docente no âmbito dos sistemas informação.
- 4
- 0
- 6
- 2
- IPLUSO6382-23557
- Data Warehouse and Business Intelligence
- 23557
- 6382
- Computer Applications for Data Science
- Details
- Category: Discipline
- Não
- The "Innovation and Entrepreneurship" course focuses on raising and cultivating an entrepreneurial mindset, integrating innovative thinking with specific technical skills in each area. The range of topics covered in this course includes identifying market opportunities, developing innovative ideas, and turning those ideas into products with a focus on digital services. The field of intervention covers concepts of management, digital marketing, social and organisational psychology, rapid prototyping, and business model validation. In the current context of rapid digitisation and constant technological evolution, having skills in entrepreneurship and innovation is crucial. This course is particularly relevant: equipping students not only to be technical professionals, but also leaders and innovators in the digital world.
- Semestral
Descrição
Data limite
Ponderação
Reflexão escrita 1 (individual)
29-10-2025
7,5%
Reflexão escrita 2 (individual)
10-12-2025
7,5%
Apresentação de pitch (pequeno grupo)
26-11-2025
15%
Projeto final e apresentação (pequeno grupo)
05-01-2026
(entrega)
07-01-2026
(apresentação)
25%
Teste
14-01-2026
20%
Participação nas dinâmicas
durante a UC
15%
- Fundamentals of Entrepreneurship and Innovation Fundamental concepts, historical evolution, entrepreneur profiles, and main typologies in the contemporary context. Identifying Opportunities Market analysis, trends, and identification of needs and underexploited niches Analysis of social and organisational behaviour in identifying opportunities. Financing and Investor Relations Sources of capital, proposal preparation, and recommended practices for presentation and communication in pitching contexts. Project Management and Leadership Organisational tools, agile methodologies, and principles of coordination and teamwork applied to innovative projects. Challenges and Case Studies Analysis of real cases, identification of critical success factors, and reflection on transferable learning for projects developed by students.
- Conhecimentos: Os estudantes desenvolverão e adquirirão uma compreensão aprofundada dos princípios fundamentais do empreendedorismo e da inovação, tornando-se aptos a identificar oportunidades e a gerar, desenvolver e validar, familiarizados com ideias com potencial de mercado. Aptidões: Ao longo do processo de aprendizagem, os estudantes desenvolverão a capacidade de criar e validar modelos de negócio, utilizar técnicas de prototipagem rápida para produtos ou serviços digitais e aplicar estratégias de marketing digital adequadas ao posicionamento e crescimento das soluções desenvolvidas. Competências: Serão capacitados a liderar projetos inovadores, tomar decisões estratégicas fundamentadas e adaptar-se a ambientes em constante transformação. No final da UC estarão preparados para enfrentar desafios reais do mercado, criar soluções tecnológicas de valor e posicionar-se como líderes e agentes de inovação na indústria digital.
- Mandatory
- Problem-Based Learning (PBL): Proposing real market challenges, encouraging students to find creative and applicable solutions. Simulations and Role-Playing: Activities where students take on the roles of entrepreneurs, investors or customers, experiencing business scenarios. Mentoring and Networking: Interaction with established professionals and real entrepreneurs, providing valuable insights and connections.
- Português
- Correia, V., & Martens, P. (2020). Empreendedorismo digital e gestão de projetos: uma revisão sistemática da literatura. Iberoamerican https://submissao.singep.org.br/8singep/arquivos/263.pdf Jablin, F. M., & Putnam, L. (1990). Organizational communication. Human communication: Theory and research, 156-182. https://www.researchgate.net/profile/Noshir-Contractor/publication/274073759_Emergence_of_Communication_Networks/links/551472f00cf283ee083622a6/ Robbins, S. P., & Judge, T. (2009). Organizational behavior. Pearson South Africa. Willness, C., & Bruni-Bossio, V. (2017). The curriculum innovation canvas: A design thinking framework for the engaged educational entrepreneur. Engagement, 21(1), 134-164. https://openjournals.libs.uga.edu/jheoe/article/view/1320
- 4
- 0
- 3
- 2
- IPLUSO6382-10784
- Innovation and Entrepreneurship
- 10784
- 6382
- Computer Applications for Data Science
- Details
- Category: Discipline
- Não
- The Curricular unit "Storage for Big Data" is a key part of the professional technical course in Computer Applications for Data Science. This course unit aims to teach students about the principles, technologies, and strategies involved in storing large volumes of data generated in Big Data environments. In the field of action, this UC focuses on advanced storage techniques for Big Data, scalable solutions, and cloud storage. The scope of work also includes data strategies and management, such as compression and partitioning. The intervention domain covers up-to-date tools and frameworks that are pillars in the world of data analysis. Given the increasing relevance of data in strategic decisions in various industries, this UC is essential in the study cycle, preparing students to become specialists capable of extracting valuable insights from raw data and turning them into impactful solutions.
- Semestral
Descrição
Data limite
Ponderação
1º Teste de avaliação
11-11-2025
35%
2º Teste de avaliação
06-01-2026
35%
Projecto
21-01-2026
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...
- Big Data. Concepts and Terminology. Characteristics of Big Data. Different Types of Data. Business Motivations for Big Data Adoption. Big Data Planning. Big Data Analysis Lifecycle. Data Acquisition. Cloud. Privacy. Corporate Technologies. Online Transaction Processing (OLTP). Online Analytical Processing (OLAP). Extract, Transform, Load (ETL). Data Storage. Big Data Storage. Clusters. Hadoop HDFS. NoSQL. Sharding. Replication. CAP Theorem. ACID. BASE. Big Data Storage Technology. Disk Storage Devices. In-Memory Storage Devices. Integrated Project: Development of a data science project from start to finish, utilizing the acquired concepts and tools.
- Knowledge: Students will deepen their understanding of storage technologies for big data, as well as scalability and distribution techniques. Data management strategies and data security and privacy. Skills: They will be empowered to manipulate large datasets, apply transformations, feature engineering, and optimize models for superior performance in real-world environments. Competencies: Students will develop the ability to conduct complete data science projects, from data collection and cleaning to data storage and security, using modern tools and frameworks. They will be prepared to tackle complex challenges in the field of data storage, translating insights into strategic recommendations and data-driven solutions for organizations.
- Mandatory
- Practical Labs in Cloud Environments: Access to cloud platforms for real and scalable experimentation with storage and datasets. Interactive Peer Review: Collaborative analysis and feedback of projects between students themselves, promoting mutual learning. Immersion Journeys: Intensive sessions where real company problems are presented to students for real-time solutions. Project-Based Learning: Development of projects that address the entire data science lifecycle, from acquisition to presentation of insights.
- Português
- Santos Maribel & Carlos Costa. BIG DATA concepts, warehousing, and Analytics. River Publishers. 2020. Thomas Erl & Paul Buhler. Big Data Fundamentals, concepts, Drives & Techniques. Service Tech Press. 2016.
- 4
- 0
- 5
- 2
- IPLUSO6382-23556
- Storage for Big Data
- 23556
- 6382
- Computer Applications for Data Science
