1. To gain and understand knowledge that provides a basis or opportunity for originality in developing and/or applying ideas, often in the context of research
2. That the students can apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their field of study.
3. That students are able to integrate knowledge and handle the complexity of formulating judgments based on the incomplete or limited information that include reflecting on the social and ethical responsibilities linked to the application of their knowledge and judgments
4. That student can communicate their conclusions and underlying knowledge to specialists and non-specialists clearly and unambiguously.
5. That students possess the learning skills that enable them to continue studying autonomously.
1. Employ statistical concepts in developing methods to analyze real problems where samples have great importance for their solution.
2. Use of free software for statistical analysis such as R and/or Python.
3. Use of multivariate statistical concepts to relate stochastic phenomena based on observed data
4. Use of Bayesian Analysis to develop and apply complex models for dependent samples.
5. Use of stochastic process knowledge to develop and analyze the real problems in which the prediction of a response variable is important.
6. Use of non-parametric models to interpret and predict random outcomes
7. Use of optimization technique for parameter estimation in complex statistical models
8. Identify the appropriate statistical analysis for a specified knowledge objective given the collected data
9. Apply the statistical model to the relevant problem in scientific research
10. Use of models for supervised and unsupervised learning Modeling of complex data via conditional stochastic dependence