|Responsible unit:||Department of Mathematics|
|Course/CS Responsible:||Master's degree in Data Science|
|Acronym||No. of Students||Study Plan||Curricular Years||Credits UCN||Credits ECTS||Contact hours||Total Time|
|M:DS||6||Official Study Plan since 2018_M:DS||1||-||6||42||162|
|Laura Luciana Cavalcante de Sousa|
Acquire a solid knowledge in inductive statistics and develop capacities and skills in statistical modelling techniques, fundamental to the presentation, analysis and interpretation of data sets.
Upon completing this course, the student should:
- have a deep understanding of the fundamental concepts and principles of statistics;
- know the fundamental parametric and nonparametric statistical methods, how to apply them to concrete situations and be able to interpret and communicate the obtained results;
- be able to use the programming language R to analyze different types of data and solve statistical problems.
Basic concepts of probability theory and some probabilistic models (structured review during the course).
Statistical models. Exponential families. Data reduction: Sufficiency and completeness.
Point and intervalar estimation. Maximum likelihood principle. Derivation and comparison of estimators. Minimum variance unbiased estimation and efficiency. Large sample theory, Confidence regions.
Simulation based inference. Resampling methods. Randomization tests.
Nonparametric inference. Order statistics and the vector of ranks. Goodness of fit tests. Rank-based tests. Measures and tests of association for two variables.Parametric hypotheses testing. Optimality criteria. Likelihood ratio tests.
Lectures TP where the topics of the syllabus are presented, exercises and related problems form the Problem Sheets are solved and discussed. The concepts and methods are illustrated and motivated by examples of different kinds. Both the theoretical developments of the methods and their application in practice are considered, using whenever appropriate, the statistical software R.
Project work to be developed in team. The discussion of the works is open, all students are encouraged to participate.
All resources are available for students at the unit’s web page.
|Trabalho prático ou de projeto||40,00|
|Frequência das aulas||56,00|