Checking date: 19/04/2024


Course: 2024/2025

Multivariate Analysis
(17756)
Master in Statistics for Data Science (Plan: 386 - Estudio: 345)
EPI


Coordinating teacher: GRANE CHAVEZ, AUREA

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Mathematics for Data Science Probability Statistical Inference Programming in R Numerical Methods for Data Science
Objectives
The main course objectives are: 1.Understand and analyze multidimensional data sets, including techniques for handling and interpreting data in multiple dimensions. 2. Gain proficiency in principal component analysis, a method for reducing the dimensionality of data while preserving its important features. 3. Explore various distance measures and joint metrics used to quantify similarities and differences between data points in multidimensional space. 4. Learn and apply multidimensional scaling techniques to visualize and understand the underlying structure of complex data sets. 5. Develop the skills to perform cluster analysis, a method for identifying meaningful groups within data based on similarity. 6. Study correspondence analysis and its application in exploring relationships between categorical variables in multidimensional data.
Skills and learning outcomes
Description of contents: programme
1. Multidimensional data sets 2. Principal component analysis 3. Distances and joint metrics 4. Multidimensional scaling 5. Cluster analysis 6. Correspondence analysis
Learning activities and methodology
Learning activities: Theoretical classes Practical classes Tutorials Team work Individual work of the student In-person evaluation tests Methodology to be used: Theoretical classes with support material available on Aula Global. Problem solving classes. Computational practices in computer rooms. Oral exhibitions Tutorial regime: Individual tutorials throughout the course.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • Alan Julian Izenman. Modern Multivariate Statistical Techniques. Springer. 2008
  • Richard A. Johnson and Dean W. Wichern. Applied Multivariate Statistical Analysis. Pearson Education. 2007

The course syllabus may change due academic events or other reasons.