Teacher(s)

Language

French

Prerequisites

Concepts and tools equivalent to those taught in teaching units

LSTAT2011 | Eléments de mathématique pour la statistique |

LSTAT2014 | Eléments de probabilités et de statistique mathématique |

Main themes

The course presents an overview of the main tools of exploratory multivariate data analysis via factorial methods. The data is projected onto a low-dimensional subspace while retaining maximum information. This reduction in dimension facilitates visualization and aids in the discovery of information and patterns in a data table.

- Reminders of algebra and geometry useful for data analysis
- Basic principles of factorial methods
- Principal component analysis
- Classification: moving averages and hierarchical classification
- Linear discriminant analysis
- Simple and multiple correspondence analysis
- Principal component regression
- Partial least squares regression

Learning outcomes

| |

1 |
General objectives. Presentation of the modern techniques for the analysis of huge multivariate data sets. Developing the basic tools for " data mining ". Specific objectives. At the end of this course, the students should be able to: - Manipulate and describe the information contained in huge data sets; - Understand why such or such method is appropriate; - Give a correct interpretation of the resulting pictures and of the output of the software; - Solve problems with real data sets. |

Content

- Data matrices
- Principal component analysis
- Classification: k-means clustering and hierarchical clustering
- Linear discriminant analysis
- Simple and multiple correspondence analysis
- Principal component regression
- Partial least squares regression

Teaching methods

During the lectures, the teacher presents the various statistical methods, covering the questions and data-sets to which they apply, the underlying mathematical theory, and how to program them in R. Homework assignments are given, the solution of which is discussed in the lectures too.

The tutorials take place in computer rooms and have as primary objective to allow the students to train themselves in applying the method on real data-sets in R.

The tutorials take place in computer rooms and have as primary objective to allow the students to train themselves in applying the method on real data-sets in R.

Evaluation methods

Exam (12/20):

Project (8/20):

- written, closed book, with the help of a formula list and a pocket calculator
- exercises and questions involving (small) calculcations, interpretation of computer output, and understanding of the main results and formulas

- Test 1: Data matrices and principal component analysis
- Test 2: Clustering and linear discriminant analysis

Project (8/20):

- individually or in pairs
- data application, the data being sought by the students themselves
- written report, to be submitted at a date or at dates specified during the semester
- detailed instructions will be provided in the exercise sessions and on the MoodleUCL course page

Other information

Prerequisities:

- vector and matrix calculus
- Euclidean geometry: points, spaces, orthogonality, distances, angles
- basic notions in statistiques: sample mean, (co)variance, correlation, covariance matrix, conditional probabilities, normal distribution, chi-square distribution

Online resources

All teaching material is made available through the MoodleUCL cours page: slides, exercises, software scripts. In addition, links to interesting external material are given too: on-line courses, videos, software documentation.

Bibliography

- Escofier, B. et Pagès, J. (2016): Analyses factorielles simples et multiples, 5e édition, Dunod, Paris.
- Lebart, L., Piron, M. et Morineau, A. (2006): Statistique exploratoire multidimensionnelle, 4e édition, Dunod, Paris.
- Saporta, G. (2011): Probabilités, analyse des données et statistique, 3e édition révisée, Editions TECHNIP, Paris.

Teaching materials

- matériel sur moodle

Faculty or entity

#### Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme

Sigle

Credits

Prerequisites

Learning outcomes

Master [120] in Data Science : Statistic

Master [120] in Biomedical Engineering

Master [120] in Statistics: Biostatistics

Master [120] in Mathematics

Master [120] in Statistics: General

Master [120] in Chemistry and Bioindustries

Approfondissement en statistique et sciences des données

Master [120] in Mathematical Engineering

Master [120] in Economics: General

Minor in Statistics, Actuarial Sciences and Data Sciences

Certificat d'université : Statistique et science des données (15/30 crédits)