After being approved in the course, the student should have the ability to:

1. Identify the phases of a Data Science Project (DSP).
2. Identify the data type and learn methods for its preparation and preprocessing.
3. Describe a data set using descriptive statistics and graphical representations.
4. Apply linear regression models.
5. Apply generalized linear models and generalized additive models.
6. Identify nonlinear functional forms and apply nonlinear regression methods.
7. Apply the Cox model to survival analysis data.
8. Select the most appropriate models, comparatively evaluate alternative prediction models, and interpret the results obtained in performance indicators.
9. Know how to implement the models computationally and interpret the results obtained.