1. Possess and understand knowledge that provides foundations for the development and / or application of this knowledge, often, in a research context.
2. Apply the acquired knowledge to solve problems in new or unfamiliar environments within multidisciplinary contexts related to their area of study.
3. Integrate knowledge and face the complexity of formulating judgments based on information that, being incomplete or limited, should include reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments.
4. Possess learning skills that allow them to continue studying in a way that will be self-directed or autonomous.
1. Apply the theoretical foundations of the techniques for the collection, storage, treatment and presentation of information as a basis for the development and adaptation of these techniques to specific problems.
2. Identify the most appropriate data analysis techniques for each problem and apply them for the analysis, design and resolution of these problems.
3. Obtain practical and efficient solutions for problems of treatment of data sets, both individually and as a team.
4. Synthesize the conclusions obtained from these analyzes and present them clearly and convincingly, both in writing and orally.
5. Be able to generate new ideas (creativity) and anticipate new situations, in the contexts of data analysis and decision making.
6. Use skills for teamwork and to relate to others autonomously.
1. Use the basic results of statistical inference and regression as a basis for prediction methods.
2. Identify and select the appropriate software tools for the treatment of time series.
3. Use advanced statistical procedures for the treatment of time series in areas such as modeling, inference and prediction.
4. Design systems for the processing of time series, from the initial collection and filtering of them, their statistical analysis, to the presentation of the final results.