Multivariate Analysis and Unsupervised Learning
- 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.
