Course: 2024/2025

Predictive modeling

(17233)

Requirements (Subjects that are assumed to be known)

Mathematics for data analysis
Statistics for data analysis

* Basic competences
- CB6: Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.
- CB9: That students know how to communicate their conclusions and the knowledge and ultimate reasons that sustain them to specialised and non-specialised audiences in a clear and unambiguous way.
- CB10: That the students have the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
* General competences
- CG1: Ability to apply the techniques of analysis and representation of information, in order to adapt it to real problems.
- CG4: Synthesise the conclusions obtained from data analyses and present them clearly and convincingly in a bilingual environment (Spanish and English) both written and orally.
- CG5: Be able to generate new ideas (creativity) and anticipate new situations, in the contexts of data analysis and decision making.
- CG6: Use social skills for teamwork and to relate to others autonomously.
- CG7: Apply advanced techniques of analysis and representation of information, in order to adapt it to real problems.
* Specific competences
- CE1: Apply in the development of methods of analysis of real problems, advanced knowledge of statistical inference.
- CE2: Use free software such as R and Python for the implementation of statistical analysis.
- CE5: Apply the advanced statistical foundations for the development and analysis of real problems, which involve the prediction of a variable response.
- CE6: Apply nonparametric models for the interpretation and prediction of random phenomena.
- CE10: Apply statistical modeling in the treatment of relevant problems in the scientific field.
* Learning outcomes
Acquisition of knowledge on: 1) statistical-mathematical foundations of the linear regression model; 2) comparison and selection of regression models; 3) extensions of the linear regression model (penalization, nonlinear models, models with dimensionality reduction, generalized linear models, etc.); 4) big data adaptations for generalized linear models.

Skills and learning outcomes

Description of contents: programme

1. Introduction
2. Linear Regression
3. Generalized Linear Models
4. Advanced Regression

Learning activities and methodology

50% lectures with teaching materials available on the Web. The other 50% practical sessions (computer labs).

Assessment System

- % end-of-term-examination 0
- % of continuous assessment (assigments, laboratory, practicals...) 100

Calendar of Continuous assessment

Basic Bibliography

- James, G., Witten, D., Hastie, T. y Tibshirani, R.. An Introduction to Statistical Learning with Applications in R. Springer-Verlag. 2021

Additional Bibliography

- Kuhn, M. and Johnson, K.. Applied Predictive Modeling. Springer. 2013
- Li, Q. and Racine, J. S.. Nonparametric Econometrics. Princeton University Press. 2007
- Peña, D.. Regresión y Diseño de Experimentos. Alianza Editorial. 2002
- Wasserman, L.. All of Statistics. Springer-Verlag. 2004
- Wood, S. N.. Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC. 2006

The course syllabus may change due academic events or other reasons.

**More information: **Aula Global