- Details
- Category: Discipline
- Aggarwal, C. C., Reddy, C. K. (eds.) (2014), Data clustering: Algorithms and Applications. Boca Raton: CRC Press. Hair, J.F., Tatham, R.L., Anderson, R.E. & Black, W. (2009). Análise multivariada de dados. (6ª ed.). Porto Alegre: Bookman. James, G., Witten, D., Hastie, T., Tibshirani, R. (2013), An Introduction to Statistical Learning: with applications in R, New York: Springer. Maindonald, J. & Braun, W. J. (2010). Data analysis and graphics using R: an example-based approach. (3rd ed.). United Kingdom: Cambridge University Press.
- Data Science
- 6321
- 23085
- Multivariate Analysis and Unsupervised Learning
- ISMAT6321-23085
- 2
- 8
- 0
- 12
- Não
- Português
- 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) for solving exercises with R and searching for solutions to proposed problems (problem based learning). Real datasets and statistical and machine learning software are used to explore patterns, cluster observations and interpret multivariate data structures.
- Mandatory
- At the end of this course unit, students should be able to: LO1: Characterize and correctly interpret multivariate data; LO2: Identify the multivariate data analysis techniques appropriate to each type of problem and the nature of the data; LO3: Apply multivariate techniques to reduce data dimensionality; LO4: Apply cluster analysis techniques; LO5: Use computational resources, such as R, Python and SAS.
- S1: Random vectors. Mean vector and covariance matrix S2: Visualization of multivariate data S3: Multivariate normal distribution S4: Dimensionality reduction techniques: principal components analysis, factor analysis and correspondence analysis S5: Hierarchical and non-hierarchical clustering methods S6: Using software, such as R, Python and SAS
A avaliação de conhecimentos é feita por avaliação contínua ou por prova escrita de exame final. A avaliação contínua inclui a realização de um teste escrito (50%) e um trabalho de grupo (50%).
- Semestral
- The curricular unit Multivariate Analysis and Unsupervised Learning belongs to the scientific area of Statistics and Data Science and addresses methods for multivariate data analysis and pattern discovery in datasets without a response variable. It develops skills in dimensionality reduction, clustering and the interpretation of complex data structures.
- Details
- Category: Discipline
- IT Engineering
- 587
- 5140
- Final Course Work
- ISMAT587-5140
- 3
- 18
- 0
- 12
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Design for Circular Economy
- 6334
- 23126
- Economic Dimensiono of Circular Economy
- ISMAT6334-23126
- 2
- 5
- 0
- 12
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Tourism Management
- 6029
- 21621
- Technology and Information Systems in Tourism
- ISMAT6029-21621
- 1
- 5
- 0
- 12
- Não
- Português
- Semestral
- Details
- Category: Discipline
- Hill, M. & Santos, M. (2015). Investigação Operacional - Vol. 1: Programação Linear. (3ª ed.). Lisboa: Edições Sílabo. Hill, M. & Santos, M. (2015). Investigação Operacional - Vol. 2: Exercícios de Programação Linear. (3ª ed.). Lisboa: Edições Sílabo. Hill, M., Santos, M. & Monteiro, A. (2015). Investigação Operacional - Vol. 3: Transportes, Afectação e Optimização de Redes. (2ª ed.). Lisboa: Edições Sílabo.
- Data Science
- 6321
- 81
- Operational Research
- ISMAT6321-81
- 2
- 5
- 0
- 12
- Não
- Português
- 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, with and without the use of a computer, in a problem-based learning approach.
- Mandatory
- At the end of this curricular unit, students should be able to: LO1: Execute the mathematical formulation of problems in the Management area; LO2: Identify appropriate Operational Research techniques for problem solving; LO3: Apply Operational Research techniques in practical cases, with and without software; LO4: Critically analyze the results obtained through Operational Research techniques, taking into account the context of the problems.
- S1: Introduction to Operational Research; S2: Formulation of Linear Programming Problems; S3: Graphical Resolution of Linear Programming Problems; S4: Simplex Algorithm for Linear Programming Problem Solving; S5: Problem solving with artificial variables; S6: Duality in Linear Programming; S7: Sensitivity analysis and post-optimization; S8: Transport problems and affectation problems; S9: Network optimization; S10: Use of software for the application of Operational Research techniques.
A avaliação de conhecimentos é feita por avaliação contínua ou por prova escrita de exame final. O regime de avaliação preferencial é o da avaliação contínua, ao longo do semestre letivo, constituída pela realização de dois testes escritos (individuais) com uma ponderação de 35% cada, um trabalho de grupo (de dois ou três alunos) com uma ponderação de 20% e participação ativa nas aulas com uma ponderação de 10%.
- Semestral
- The curricular unit Operational Research belongs to the scientific area of Mathematics and focuses on quantitative methods for decision support. It addresses the formulation and solution of optimization problems using mathematical models applied to management, logistics, planning and resource allocation. The acquired knowledge is relevant to Data Science, enabling the selection of efficient solutions in real decision-making contexts.
