Tópicos de Análise de Dados
This course aims to provide doctoral students with a deep understanding of the foundations and advanced approaches in statistical data analysis, with particular emphasis on the distinction between supervised and unsupervised methods, parametric and non-parametric approaches, and an introduction to the Bayesian modeling paradigm.
Throughout the course, the theoretical principles underlying these methodologies will be explored, along with their practical applications in real-world contexts. Students will be encouraged to develop a critical and integrated perspective on modern statistical modeling through the analysis, implementation, and interpretation of methods in appropriate computational environments.
Given the technical and quantitative nature of the course, prior knowledge of statistics, linear algebra, calculus, and basic programming skills (preferably in R) is recommended.
The bibliography and study materials are predominantly in English.
Data analysis
Statistical learning
Supervised and unsupervised methods
Bayesian modeling
- Understand and apply supervised and unsupervised data analysis methods;
- Distinguish between parametric and non-parametric approaches in statistical modeling;
- Explore and interpret latent structures within datasets;
- Implement and analyze predictive models, including basic Bayesian models;
- Critically evaluate results and methodological decisions in data analysis contexts.
1) Foundations of data analysis and statistical modeling
2) Unsupervised statistical methods
3) Supervised statistical methods
4) Bayesian modeling
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R (2nd ed.). Springer.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.
E-learning
Evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (60%) and final evaluation (40%). Further information is detailed in the Learning Agreement of the course unit.