COMPETENCES THAT THE STUDENT ACQUIRES WITH THIS SUBJECT
CB7: That students know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study.
CB9: That students know how to communicate their conclusions and the knowledge and ultimate reasons that sustain them to specialized and non-specialised audiences in a clear and unambiguous way.
CG1: Know and apply the theoretical foundations of the techniques of analysis and representation of information, in order to adapt it to real problems.
CG2: Identify the most appropriate statistical model for each real problem and know how to apply it for the analysis, design and solution of it.
CG3: Obtain scientifically viable solutions for real statistical problems, both individually and as a team.
CG4: Synthesise the conclusions obtained from statistical analyses and present them clearly and convincingly in a bilingual environment (Spanish and English) both written and orally.
CG7: Know and apply the theoretical foundations of the techniques of analysis and representation of information, in order to adapt it to real problems.
- Specific competences:
CE2: Use free software such as R and Python for the implementation of statistical analysis.
CE9: Identify correctly the type of statistical analysis corresponding to certain objectives and data.
CE10: Apply statistical modeling in the treatment of relevant problems in the scientific field.
CE13: Apply models for supervised and unsupervised learning.
CE14: Modelling complex data with stochastic dependence.
KNOWLEDGE ACQUISITION: 1) basis representation of functional data; 2) dimension reduction techniques for functional data 3) linear regression model with functional predictor; 4) classification with functional data.