Course guide of Computational Statistics (27011E2)
Grado (bachelor's degree)
Branch
Module
Subject
Year of study
Semester
ECTS Credits
Course type
Teaching staff
Theory
Practice
- Pablo Pedro Jurado Bascón Grupo: 1
- María Dolores Martínez Miranda Grupo: 2
Prerequisites of recommendations
It is recommended to have basic knowledge on Probability and Mathematical Statistics.The course builds on the knowlegde from the subjects Descriptive Statistics and Introduction to Probability and Statistical Inference in the first and third year, respectively, of the degree.
Brief description of content (According to official validation report)
- Statistical Computing and Computational Statistics. Evolution of Computational Statistics
- Software for statistical analyses and programming
- The R environment for statistical computing and graphical analysis
- Data structures for statistical analyses
- Design and analysis of statistical problems with real data
General and specific competences
General competences
- CG01. Poseer los conocimientos básicos y matemáticos de las distintas materias que, partiendo de la base de la educación secundaria general, y apoyándose en libros de texto avanzados, se desarrollan en esta propuesta de título de Grado en Matemáticas
- CG02. Saber aplicar esos conocimientos básicos y matemáticos a su trabajo o vocación de una forma profesional y poseer las competencias que suelen demostrarse por medio de la elaboración y defensa de argumentos y la resolución de problemas dentro de las Matemáticas y de los ámbitos en que se aplican directamente
- CG03. Saber reunir e interpretar datos relevantes (normalmente de carácter matemático) para emitir juicios que incluyan una reflexión sobre temas relevantes de índole social, científica o ética
- CG04. Poder transmitir información, ideas, problemas y sus soluciones, de forma escrita u oral, a un público tanto especializado como no especializado
- CG05. Haber desarrollado aquellas habilidades de aprendizaje necesarias para emprender estudios posteriores con un alto grado de autonomía
- CG06. Utilizar herramientas de búsqueda de recursos bibliográficos
Specific competences
- CE01. Comprender y utilizar el lenguaje matemático. Adquirir la capacidad de enunciar proposiciones en distintos campos de las matemáticas, para construir demostraciones y para transmitir los conocimientos matemáticos adquiridos
- CE03. Asimilar la definición de un nuevo objeto matemático, en términos de otros ya conocidos, y ser capaz de utilizar este objeto en diferentes contextos
- CE04. Saber abstraer las propiedades estructurales (de objetos matemáticos, de la realidad observada, y de otros ámbitos) y distinguirlas de aquellas puramente accidentales, y poder comprobarlas con demostraciones o refutarlas con contraejemplos, así como identificar errores en razonamientos incorrectos
- CE05. Resolver problemas matemáticos, planificando su resolución en función de las herramientas disponibles y de las restricciones de tiempo y recursos
- CE06. Proponer, analizar, validar e interpretar modelos de situaciones reales sencillas, utilizando las herramientas matemáticas más adecuadas a los fines que se persigan
- CE07. Utilizar aplicaciones informáticas de análisis estadístico, cálculo numérico y simbólico, visualización gráfica, optimización u otras para experimentar en matemáticas y resolver problemas
- CE08. Desarrollar programas que resuelvan problemas matemáticos utilizando para cada caso el entorno computacional adecuado
Transversal competences
- CT01. Desarrollar cierta habilidad inicial de "emprendimiento" que facilite a los titulados, en el futuro, el autoempleo mediante la creación de empresas
- CT02. Fomentar y garantizar el respeto a los Derechos Humanos y a los principios de accesibilidad universal, igualdad ante la ley, no discriminación y a los valores democráticos y de la cultura de la paz
Objectives (Expressed as expected learning outcomes)
- To know the methodology for statistical computing and graphical analysis with the environment R
- To perform basic statistical analyses with data.
- To apply standard statistical modelling techniques including linear models
- To develop a basic knowledge of core topics in Computational Statistics
Detailed syllabus
Theory
- Introduction
- The R environment for statistical analysis and programming
- Essentials of the R Language
- Data structures and basic objects
- Writing dynamic documents
- Programming in R. Creating functions
- Function objects
- Basic programming structures
- Methodology for the statistical analysis with R
- Graphics
- Statistical models
- Case studies
- Simulation and Bootstrap
- Simulation of random variables
- The Bootstrap principle and some applications in statistical inference
- Introduction to nonparametric curve estimation
- Density functions
- Regression functions
Practice
- Data structures and basic objects in R
- Basic programming and creating new functions in R
- Methodology for the statistical analysis with R: graphics and statistical modelling
- Simulation and Bootstrap: Applications using R
- Nonparametric estimation of density and regression functions using R
Bibliography
Basic reading list
- Crawley - The R Book, 2nd ed (2012)
- Crawley - Statistics: An Introduction Using R, 2nd ed. (2015)
- Gentle, J.E. (2009). Computational Statistics. Statistics & Computing. Springer
Complementary reading
- Chambers, J.M. (2008). Software for Data Analysis: Programming with R. Springer.
- Davison, A.C. and Hinkely, D.V. (1997). Bootstrap Methods and Their Applications. Cambridge University Press.
- Deepayan, S. (2008). Lattice: Multivariate Data Visualization with R. Springer.
- Heineman, G., Pollice, G. y Selkow, S. (2016). Algorithms in a Nutshell. Second Edition. O’Reilly Media Inc.
- Lafaye de Micheaux, P., Drouilhet, R. y Liquet, B. (2014). The R Software. Fundamentals of Programming and Statistical Analysis. Springer.
- Robert, C.P. y G. Casella (2010). Introducing Monte Carlo Methods with R. Springer.
- Templ, M. (2016). Simulation for Data Science with R. Packt Publishing.
- Wand, M.P. y Jones, M.C. (1995). Kernel Smoothing. Volume 60 of Monographs on Statistics and Applied Probability. Chapman & Hall, Ltd., London.
- Wasserman, L. (2006). All of Nonparametric Statistics. Springer Texts in Statistics. Springer-Verlag, New York.
- Wickham, H. (2019). Advanced R. The R Series. Chapman & Hall/CRC.
- Wickham,H. (2016). ggplot2. Elegant Graphics for Data Analysis. Springer.
- Wickham, H. y Grolemund, G. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media. Canada.
- Xie, Y. (2015). Dynamic Documents with R and knitr. Second Edition. Chapman & Hall. CRC Press.
Recommended links
- The R Project for Statistical Computing. http://www.r-project.org
- RStudio. http://www.rstudio.com
Teaching methods
- MD01. Lección magistral/expositiva
- MD02. Sesiones de discusión y debate
- MD03. Resolución de problemas y estudio de casos prácticos
- MD04. Prácticas en sala de informática
- MD05. Seminarios
- MD06. Análisis de fuentes y documentos
- MD07. Realización de trabajos en grupo
- MD08. Realización de trabajos individuales
- MD09. Seguimiento del TFG
Assessment methods (Instruments, criteria and percentages)
Ordinary assessment session
This module will be assessed by examination and courseworks.
Assessment pattern:
| Assessment type | Weighting | Mininum qualifying mark | |
|---|---|---|---|
| Part written and part computer-based exam: exercises and tasks using R and short-answer questions | 40% | 0 | |
| Group coursework (a final project developing further the course contents) | 30% | 0 | |
| Individual tasks (set of exercises and tasks using R), including an assessment of the student participation, attitude and engagement during the lectures and lab sessions | 30% | 0 |
The pass mark for the course is 5, as established in the regulation of evaluation of students at the University of Granada. Any minimum qualifying marks for specific assessments are listed in the table above. The weighting of the different components can also be found above.
Extraordinary assessment session
Part written and part computer-based examination consisting of theoretical questions and practical exercises on the topics described in the theory and practical syllabus.
Single final assessment
Part written and part computer-based examination consisting of theoretical questions and practical exercises on the topics described in the theory and practical syllabus.
Additional information
Use of Artificial Intelligence: If AI tools are used as part of this course, students are expected to use them ethically and responsibly. Students should follow the recommendations set out in the document Recommendations for the Use of Artificial Intelligence at the University of Granada, available at:
https://ceprud.ugr.es/formacion-tic/inteligencia-artificial/recomendaciones-ia#contenido0
Plagiarism: Plagiarism, defined as submitting work created by another person as one's own or reproducing text without properly acknowledging its source and presenting it as original work, will automatically result in a final grade of 0 (fail) for the course in which the plagiarism is detected, regardless of any other marks obtained by the student. This sanction is without prejudice to any disciplinary action that may be taken under the University's regulations.
Intellectual Property: The University of Granada promotes respect for intellectual property and seeks to instill in students the understanding that plagiarism is incompatible with the principles of higher education. Accordingly, authorship of academic work will be recognized and protected in accordance with current intellectual property legislation.
Adaptation of Course Content: Any aspect of this course guide may be adapted to accommodate the diverse needs of students, taking into account the recommendations provided by tutors for students with Specific Educational Support Needs (NEAE) and in accordance with the University of Granada regulations: https://www.ugr.es/sites/default/files/2017-09/NCG1114.pdf
Applicable Regulations: Assessment and grading are governed by the Regulations on the Assessment and Grading of Students at the University of Granada, available at: https://www.ugr.es/universidad/normativa/texto-consolidado-normativa-evaluacion-calificacion-estudiantes-universidad-granada
Information for Students with Disabilities and/or Specific Educational Support Needs (NEAE): Information on available support services and accommodations can be found at: https://ve.ugr.es/servicios/atencion-social/estudiantes-con-discapacidad
Información de interés para estudiantado con discapacidad y/o Necesidades Específicas de Apoyo Educativo (NEAE): Gestión de servicios y apoyos (https://ve.ugr.es/servicios/atencion-social/estudiantes-con-discapacidad).
Software Libre
R: http://www.r-project.org
RStudio. http://www.rstudio.com