* Basic competences
- CB6: Possess and understand the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.
- CB9: Communicate conclusions, as well as the knowledge and the ultimate reasons that support them, to specialized and non-specialized audiences in a clear and unequivocal manner.
- CB10: Develop the learning skills that enable further study in a manner that is largely self-directed or autonomous.
* General competences
- CG1: Apply the techniques of analysis and representation of information, to adapt it to real problems.
- CG4: Synthesize the conclusions obtained from data analysis and present them clearly and convincingly in a bilingual environment (Spanish and English), both written and oral.
- CG5: Generate new ideas (creativity) and anticipate new situations, in the contexts of data analysis and decision making.
- CG6: Apply social skills for teamwork and to relate with others in an autonomous way.
* Specific competences
- CE1: Apply advanced knowledge of statistical inference in the development of methods for the analysis of real problems.
- CE2: Use free software such as R and Python for the implementation of statistical analysis.
- CE5: Apply advanced statistical fundamentals for the development and analysis of real problems involving the prediction of a variable response.
- CE6: Apply nonparametric models for the interpretation and prediction of random phenomena.
- CE10: Apply statistical modeling in the treatment of relevant problems in the scientific field.
* Learning outcomes
Acquisition of knowledge on: 1) kernel density estimators and their applications; 2) nonparametric regression methods based on smoothing; 3) nonparametric hypothesis testing.