2018-2019 / MATH2021-1

High-dimensional data analysis


15h Th, 10h Labo., 15h Proj.

Number of credits

 Master in data science (120 ECTS)3 crédits 
 Master in data science and engineering (120 ECTS)3 crédits 


Gentiane Haesbroeck

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The theoretical course is devoted to the following themes:
- Multivariate summary statistics and graphics - Estimation of the covariance matrix: classical ML technique, penalized version and robust version - Exploratory analyses: Principal component analysis  and clustering - Multivariate ranks and quantiles

Learning outcomes of the learning unit

The student will gain sufficient knowledge to be able to select the appropriate multivariate technique to reduce the dimension of the problem or construct classification rules,...

Prerequisite knowledge and skills

A strong background in univariate statistics is required. Moreover, even though the mathematical justifications are not developped in details, the students must be familiar with the basic notions of linear algebra (vector, matrix, detemrinant, eigen valeurs and eigen vectors...).

Planned learning activities and teaching methods

Practicals include data analysis with the statistical package R.

Mode of delivery (face-to-face ; distance-learning)

The course is officially scheduled on Wednesday PM in the first semester. A more detailed planning will be distributed at the beginning of the lectures. 

Recommended or required readings

There are no lecture notes. The slides will be available from eCampus. Moreover, for each them, a reference book will be notified in order to suggest additionnal reading.

Assessment methods and criteria

The final grade is a weighted mean computed on the grades obtained for the personal homeworks given during the semester and fpr the exam consisting of a data analysis to be performed in the computer room.

Work placement(s)

Organizational remarks

The lectures are taught in English.


Lecturer: Gentiane HAESBROECK, Institute of Mathematics (B37), g.haesbroeck@ulg.ac.be