Checking date: 02/04/2024

Course: 2024/2025

Predictive modeling
Master in Big Data Analytics (Plan: 352 - Estudio: 322)


Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


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.

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