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Tronc commun
Statistical modelling
Cours au choix
At least 2 courses among the 5 following.
Machine learning and Data mining
Cours au choix
Choose at least 2 courses among the 3 following.
Statistical computing, data structures and algorithms for data analysis
Cours au choix
EN
q1
30h+22.5h
5
credits
Teacher(s):
> Jean-Charles Delvenne (coord.)
> Gautier Krings (compensates Vincent Blondel)
Philosophie
Maximum one course among:
Activités de base
The student chooses, for a maximum of 10 credits, the courses in the list below for which it did not acquire equivalent skills in its previous formation. This choice is discussed with the advisor of the master and next approved by the restricted jury.
Mathématique - Analyse et algèbre linéaire
Each of the following three modules of two courses allows acquiring similar skills:
Module 1
Module 2
Module 3
Probabilités et Statistique
Each of the following four modules of two courses allows acquiring similar skills:
Module 1
Module 2
Module 3
Module 4
Programmation et informatique
The student must acquire the skills bound to these three courses:
Other pre-requisite activities
The teaching units below may be added to the student's program if they are admitted on a case-by-case basis. The choice of these units will be made in consultation with the study advisor.
FR
q1
22.5h+15h
4
credits
Teacher(s):
> Aurélie Bertrand (compensates Eugen Pircalabelu)
> Aurélie Bertrand (compensates Bernadette Govaerts)
Aurélie Bertrand (compensates Eugen Pircalabelu), Aurélie Bertrand (compensates Bernadette Govaerts),
EN
q1 or q2
20h
3
credits
Teacher(s):
> Stéphanie Brabant
> Jean-Luc Delghust
> Aurélie Deneumoustier
> Fanny Desterbecq
> Charlotte Diaz
> Marie Duelz
> Jérémie Dupal
> Ilenia Gallo
> Adrien Kefer (compensates Laura Lievens)
> Sandrine Mulkers (coord.)
> Marc Piwnik (coord.)
> Nevin Serbest
> Françoise Stas
> Anne-Julie Toubeau
Stéphanie Brabant, Jean-Luc Delghust, Aurélie Deneumoustier, Fanny Desterbecq, Charlotte Diaz, Marie Duelz, Jérémie Dupal, Ilenia Gallo, Adrien Kefer (compensates Laura Lievens), Sandrine Mulkers (coord.), Marc Piwnik (coord.), Nevin Serbest, Françoise Stas, Anne-Julie Toubeau,
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Professional Focus [30.0]
Content:
FR
q1 or q2
20
credits
Optionnal course
Choose 1 course among the 2 following.
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Options
The student completes his program with elective courses reported in the list below. With the agreement of the restricted jury, the student can also complete his program by other courses that he would consider relevant and taught at the UCLouvain. The student may include a maximum of 5 language course credits in his or her program, provided that the level is appropriate and consistent with the student's and the program's profile.
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Data in action
Content:
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Data sciences en linguistique et Text Mining
Content:
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Algorithme, informatique, optimisation, recherche opérationnelle
Content:
Cours au choix
Maximum one course among the two courses (As they are bachelor course, the amount of credits is reduced to 5)
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Stage
1 internship maximum, chosen among the two following (optional):
Content:
FR
q1 or q2
10
credits
FR
q1 or q2
5
credits
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Data Sciences appliquées à la gestion
The following courses are taught on two-month periods and the first three ones are taught on the Campus of UCL Mons. Thus, we ask to students to check that this choice is compatible with their schedule, before inscription.
Content:
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Optional courses
These credits are not counted within the 120 required credits.
Content:
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Preparatory Module
(only for students who qualify for the course via complementary coursework)
To access this Master, students must have a good command of certain subjects. If this is not the case, they must add supplementary classes at the beginning of their Master’s programme in order to obtain the prerequisites for these studies.
To access to this Master's degree, the student has to master a minimum of preliminary skills in mathematics, programming, algorithmic and probability-statistics. If it is not the case, additional teachings must be added to his program. He can nevertheless include a maximum of 10 of these credits in the prerequisite module planned in the common-core syllabus of the Master's degree.
Students who do not have a B1 level in English (level obtained at UCLouvain) must take the LANGL1330 English course. A dispensatory test is organized at the beginning of the academic year.
The student is invited to meet the program advisor to decide which courses should be followed. The restricted jury must next approve his program.
Mathématique - Analyse et algèbre linéaire
Each of the following three modules allows acquiring similar skills:
Module 1
Module 2
Module 3
Probabilités et Statistique
Each of the following four modules allows acquiring similar skills:
Module 1
Module 2
Module 3
Module 4
Programmation et informatique
The student must acquire the skills related to these three courses:
Other pre-requisite activities
The teaching units below may be added to the student's program if they are admitted on a case-by-case basis. The choice of these units will be made in consultation with the study advisor.
FR
q1
22.5h+15h
4
credits
Teacher(s):
> Aurélie Bertrand (compensates Eugen Pircalabelu)
> Aurélie Bertrand (compensates Bernadette Govaerts)
Aurélie Bertrand (compensates Eugen Pircalabelu), Aurélie Bertrand (compensates Bernadette Govaerts),
EN
q1 or q2
20h
3
credits
Teacher(s):
> Stéphanie Brabant
> Jean-Luc Delghust
> Aurélie Deneumoustier
> Fanny Desterbecq
> Charlotte Diaz
> Marie Duelz
> Jérémie Dupal
> Ilenia Gallo
> Adrien Kefer (compensates Laura Lievens)
> Sandrine Mulkers (coord.)
> Marc Piwnik (coord.)
> Nevin Serbest
> Françoise Stas
> Anne-Julie Toubeau
Stéphanie Brabant, Jean-Luc Delghust, Aurélie Deneumoustier, Fanny Desterbecq, Charlotte Diaz, Marie Duelz, Jérémie Dupal, Ilenia Gallo, Adrien Kefer (compensates Laura Lievens), Sandrine Mulkers (coord.), Marc Piwnik (coord.), Nevin Serbest, Françoise Stas, Anne-Julie Toubeau,
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.