* 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
- CE2: Use free software such as R and Python for the implementation of statistical analysis.
- CE7: Apply optimization techniques in the estimation of parameters in complex sampling models.
- CE8: Apply and develop visualization techniques for samples collected with open source software such as R and Python.
- CE10: Apply statistical modeling in the treatment of relevant problems in the scientific field.
* Learning outcomes
Acquisition of knowledge on: 1) combination of C++ with R; 2) parallel computing; 3) Google Cloud computing platform.