* 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.
- CB7: Know how to apply acquired knowledge and problem-solving skills in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study.
- CB8: Integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments.
- 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.
- CG2: Identify the most appropriate statistical model for each real problem and know how to apply it for its analysis, design and solution.
- CG3: Obtain scientifically viable solutions to complex real statistical problems, both individually and in teams.
- 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.
- CE8: Apply and develop visualization techniques for samples collected with open source software such as R and Python.
- CE9: Correctly identify the type of statistical analysis corresponding to specific objectives and data.
- CE10: Apply statistical modeling in the treatment of relevant problems in the scientific field.
- CE11: Formalize random phenomena and model them by means of probabilistic models.
- CE12: Apply models for supervised and unsupervised learning.
- CE13: Model complex data with stochastic dependence.
- CE14: Apply advanced knowledge and skills in statistical consulting.
* Learning outcomes
Acquisition of knowledge on: 1) skills useful in a statistical consulting service; 2) techniques for automatic presentation of results in reports; 3) development of Shiny applications; 4) the tidyverse environment; 5) the tidymodels environment.