- 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 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
- 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
- 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
- Details
- 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
