To access this Master, students must have a good command of certain subjects. If this is not the case, in the first annual block of their Masters programme, students must take supplementary classes chosen by the faculty to satisfy course prerequisites.
To enter the Master in Data Science, Information Technology orientation, the student must have a minimum of previous skills in mathematics, computer science, algorithms and probability-statistics. If this is not the case, he/she must add additional courses to his/her Master's program. The content of this additional training is determined by the program commission. The skills to be mastered correspond to those of the following courses:
Mathematics - Calculus and linear algebra
The student follows one of the following blocks:
Module 1
Module 2
FR
q2
30h+30h
5
credits
Teacher(s):
> Cécile Coyette (compensates Tom Claeys)
Cécile Coyette
(compensates Tom Claeys)
Probability and statistics
The student follows one of the following blocks:
Module 1
Module 2
FR
q1
30h+30h
5
credits
Teacher(s):
> Olivier Pereira
> Olivier Pereira (compensates Jean-Charles Delvenne)
FR
q1
30h+30h
5
credits
Teacher(s):
> Donatien Hainaut
> Donatien Hainaut (compensates Laurent Jacques)
Programming and computer science
The student follows one of the following blocks:
Un cours parmi :
EN
q1
30h+22.5h
5
credits
> French-friendly
Teacher(s):
> Vincent Blondel
> Gaëtan Cassiers (compensates Jean-Charles Delvenne)
> Thomas Peters (compensates Jean-Charles Delvenne)
Vincent Blondel
,
Gaëtan Cassiers
(compensates Jean-Charles Delvenne)
,
Thomas Peters
(compensates Jean-Charles Delvenne)
Computer systems:
The student follows one of the following blocks:
Numerical methods and optimisation:
The student follows one of the following blocks:
Un cours parmi :
Other EU to be determined with the Study Advisor
Depending on his / her previous academic background, the student (in consultation with the study advisor) can add other UEs in order to acquire the necessary prerequisites for the program.