Competencies and objectives

 

Course context for academic year 2024-25

Esta asignatura se ubica en el módulo Fundamental y dentro de él, en la materia Optimización. La asignatura recoge el estudio y aplicación de las técnicas de análisis de datos detalladas en los contenidos.

Análisis de datos II es una ampliación de la asignatura Análisis de datos I. En esta asignatura se pretende desarrollar con más detalle las técnicas de dependencia con enfoque inferencial que se introdujeron en la asignatura anterior. Por este motivo la asignatura empieza con la introducción de las distribuciones multivariantes y la inferencia sobre vectores de medias. Los modelos de regresión se verán ampliados por los modelos lineales generalizados, en los que la respuesta no sigue una distribución normal. Se abordará también el caso en el que la variable respuesta es multivariante, en el que se pretenden establecer relaciones entre dos matrices de datos. Y por último, se mostrarrán los principales algoritmos predictivos para clasificación y regresión no lineales y las principales métricas orientadas a evaluar los modelos y a comunicar resultados. Esta última parte pretende que el alumno comprenda las matemáticas que sustentan las principales técnicas del Machine Learning

Las prácticas se intercalan con las clases teóricas y se llevarán a cabo con software libre: R y Python.

 

 

Course content (verified by ANECA in official undergraduate and Master’s degrees) for academic year 2024-25

Specific Competences (CE)

  • CE1 : Understand and use mathematical language. Acquire the capacity to enunciate propositions in different fields of Mathematics, to construct demonstrations and transmit the mathematical knowledge acquired.
  • CE10 : Communicate, both orally and in writing, mathematical knowledge, procedures, results and ideas.
  • CE11 : Ability to solve academic, technical, financial and social problems using mathematical methods.
  • CE12 : Ability to work in a team, providing mathematical models adapted to the needs of the group.
  • CE14 : Solve qualitative and quantitative problems using previously developed models.
  • CE15 : Recognise and analyse new problems and prepare strategies to resolve them.
  • CE16 : Prepare, present and defend scientific reports both in writing and orally to an audience.
  • CE5 : Propose, analyse, validate and interpret models of simple real-life situations, using the most appropriate mathematical tools for the purpose.
  • CE6 : Solve mathematical problems using basic calculus skills and other techniques, planning their resolution according to the tools available and any time and resource restriction.
  • CE7 : Use computer applications for statistical analysis, numerical calculus and symbolic calculus, graphic visualisation and others to experiment in Mathematics and solve problems.
  • CE8 : Develop programmes that solve mathematical problems using the appropriate computational environment for each particular case.
  • CE9 : Use bibliographic search tools for Mathematics.

 

Specific Generic UA Competences

  • CGUA1 : Understand scientific English.
  • CGUA2 : Possess computer skills relevant to the field of study.
  • CGUA3 : Acquire or possess basic Information and Communications Technology skills and correctly manage the information gathered.

 

Generic Degree Course Competences

  • CG1 : Develop the capacity for analysis, synthesis and critical reasoning.
  • CG2 : Show the capacity for effective and efficient management/direction: entrepreneurial spirit, initiative, creativity, organisation, planning, control, decision making and negotiation.
  • CG3 : Solve problems effectively.
  • CG4 : Show capacity for teamwork.
  • CG5 : Commitment to ethics, the values of equality and social responsibility as a citizen and professional.
  • CG6 : Self-learning.
  • CG7 : Show the capacity to adapt to new situations.
  • CG9 : Show the ability to transmit information, ideas, problems and solutions to both specialist and non-specialist audiences.

 

 

 

Learning outcomes (Training objectives)

No data

 

 

Specific objectives stated by the academic staff for academic year 2024-25

No data

 

 

General

Code: 25062
Lecturer responsible:
Rebollo Múgica, Julen
Credits ECTS: 6,00
Theoretical credits: 1,00
Practical credits: 1,40
Distance-base hours: 3,60

Departments involved

  • Dept: MATHEMATICS
    Area: STATISTICS AND OPERATIONS RESEARCH
    Theoretical credits: 1
    Practical credits: 1,4
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught