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Course description
  DATA ANALYSIS I

Competencies and objectives

 

Course context for academic year 2021-22

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. En el primer bloque se describe qué es el análisis de datos dentro del contexto del proceso de investigación y se clasifican los métodos en dos tipos: métodos de interdependencia y de dependencia que dan lugar a los bloques 2 y 3 de la asignatura.

 

 

Course content (verified by ANECA in official undergraduate and Master’s degrees)

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.

 

 

 

Learning outcomes (Training objectives)

No data

 

 

Specific objectives stated by the academic staff for academic year 2021-22

No data

 

 

General

Code: 25042
Lecturer responsible:
NUEDA ROLDAN, MARIA JOSE
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