Checking date: 07/07/2020

Course: 2020/2021

Functional Data Analysis
Study: Master in Statistics for Data Science (345)

Coordinating teacher: GALEANO SAN MIGUEL, PEDRO

Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 3.0 ECTS


Students are expected to have completed
Programming in R, Stochastic Processes and Multivariate Analysis.
Competences and skills that will be acquired and learning results.
COMPETENCES THAT THE STUDENT ACQUIRES WITH THIS SUBJECT -Basic competences: CB7: That students know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study. CB9: That students know how to communicate their conclusions and the knowledge and ultimate reasons that sustain them to specialized and non-specialised audiences in a clear and unambiguous way. -General competences: CG1: Know and apply the theoretical foundations of the techniques of analysis and representation of information, in order to adapt it to real problems. CG2: Identify the most appropriate statistical model for each real problem and know how to apply it for the analysis, design and solution of it. CG3: Obtain scientifically viable solutions for real statistical problems, both individually and as a team. CG4: Synthesise the conclusions obtained from statistical analyses and present them clearly and convincingly in a bilingual environment (Spanish and English) both written and orally. CG7: Know and apply the theoretical foundations of the techniques of analysis and representation of information, in order to adapt it to real problems. - Specific competences: CE2: Use free software such as R and Python for the implementation of statistical analysis. CE9: Identify correctly the type of statistical analysis corresponding to certain objectives and data. CE10: Apply statistical modeling in the treatment of relevant problems in the scientific field. CE13: Apply models for supervised and unsupervised learning. CE14: Modelling complex data with stochastic dependence. KNOWLEDGE ACQUISITION: 1) basis representation of functional data; 2) dimension reduction techniques for functional data 3) linear regression model with functional predictor; 4) classification with functional data.
Description of contents: programme
1. Introduction to functional data analysis 2. Functional principal component analysis 3. Functional linear regression 4. Classification with functional data
Learning activities and methodology
LEARNING ACTIVITIES RELATED TO MATTERS AF1 Theoretical class AF2 Practical class AF4 Laboratory AF5 Tutoring AF6 Group work AF7 Individual work AF8 In-person evaluation tests Code activity Total hours In-person hours % In-person hours - student AF1 44 44 100 AF2 20 20 100 AF4 20 20 100 AF5 16 16 100 AF6 40 0 0 AF7 154 0 0 AF8 6 6 100 TOTAL MATTER 300 100 33 TEACHING METHODOLOGIES RELATED TO MATTERS MD1 Explanations in the theoretical class with support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students learning. MD3 Resolution of case study, problems, etc. raised by the professor individually or in groups MD5 Development of projects and reports individually or in groups
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • J.O. Ramsay and B.W. Silverman. Functional Data Analysis. Springer-Verlag. 1997, 2005
  • J.O. Ramsay and B.W. Silverman. Applied Functional Data Analysis. Springer-Verlag. 2002
  • J.O. Ramsay, G. Hooker and S. Graves. . Functional Data Analysis with R and MATLAB. Springer. 2010
  • P. Kokoszka and M. Reimherr. Introduction to Functional Data Analysis. Chapman and Hall/CRC. 2017

The course syllabus and the academic weekly planning may change due academic events or other reasons.

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