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|
Language(s) of instruction
Organisation and examination
Teaching in the first semester, review in January
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 exploratory data analysis - Principal component analysis - Clustering - Regularisation techniques - 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.
The lectures are taught in English.
Lecturer: Gentiane HAESBROECK, Institute of Mathematics (B37), firstname.lastname@example.org