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Tronc communStatistical modellingLSTAT2120 Linear modelsLSTAT2130 Introduction to Bayesian statisticsCours au choixAt least 2 courses among the 5 following.LSTAT2100 Discrete data analysis.LSTAT2170 Times seriesLSTAT2210 Mixed linear modelsLSTAT2190 Concepts and treatment of random vectorsMachine learning and Data miningLSTAT2110 Data AnalysisCours au choixChoose at least 2 courses among the 3 following.
EN
q1 30h+30h 5 credits > French-friendlyTeacher(s):
> John Lee
> John Lee (compensates Michel Verleysen)
LINFO2275 Data mining & decision makingStatistical computing, data structures and algorithms for data analysisLDATS2360 Seminar in data management: basicLINFO2172 DatabasesCours au choixLINMA2472 Algorithms in data scienceEN
q1 30h+22.5h 5 credits > French-friendlyTeacher(s):
> Jean-Charles Delvenne (coord.)
> Benoît Legat (compensates Vincent Blondel)
PhilosophieMaximum one course among:
LSC2220 Philosophy of scienceLFILO2003E Ethics in the Sciences and technics (sem)FR
q2 15h+15h 2 credits
Activités de baseTo access to this Master's degree, students must master a minimum of basic skills in mathematics, programming, algorithms and statistical probability. In consultation with the student advisor, additional courses may be required. In such cases, a maximum of 10 additional credits may be included in the core curriculum. The list of additional courses is available in the "Preparatory module" section.
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Professional Focus [30.0]Content:LDATS2840 Master thesis in data analytics
FR
q1 or q2 20 credits
LDATS2350 Data MiningOptionnal courseChoose 1 course among the 2 following.
LDATA2010 Information visualisationLINFO2364 Mining Patterns in Data -
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 actionContent:LDATS2310 Deep learning for Insurance and FinanceLSTAT2200 Survey and SamplingLSTAT2320 Design of experiment.LSTAT2340 Statistical Analyses of ¿omics DataLSTAT2380 Statistical consultingLSTAT2390 Applied statistics workshops
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Data sciences en linguistique et Text MiningContent:LINFO2263 Computational LinguisticsLFIAL2620 Natural language processingLFIAL2630 Automatic language processing methods
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Algorithme, informatique, optimisation, recherche opérationnelleContent:Cours au choixMaximum one course among the two courses (As they are bachelor course, the amount of credits is reduced to 5)
LINFO1113 Numerical algorithmicLINFO1114 Discrete MathematicsLINFO1252 Informatic SystemsLINFO2266 Advanced Algorithms for OptimizationLINFO2145 Cloud Computing -
Stage1 internship maximum, chosen among the two following (optional):Content:LDATS2940 Stage en science des données
FR
q1 or q2 10 credits
FR
q1 or q2 5 credits
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Data Sciences appliquées à la gestionThe 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:MLSMM2152 New Technologies & Emerging PracticesMLSMM2153 Web MiningMLSMM2156 Recommender SystemsLLSMS2030 Supply Chain Management
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Optional coursesThese credits are not counted within the 120 required credits.Content:LSST1001 ±õ²Ô²µÃ©²Ô¾±±ð³Ü³æ³§³Ü»åLSST1002M Information and critical thinking - MOOC
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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, in the first annual block of their Masters programme, students must take supplementary classes chosen by the faculty to satisfy course prerequisites.
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 linéaireEach of the following three modules allows acquiring similar skills:
Module 1LINFO1111 AnalysisLINFO1112 AlgebraModule 2LINGE1114 Mathematics I: analysisModule 3LMAT1101 Mathematics 1LMAT1102 Mathematics 2Probabilités et StatistiqueEach of the following four modules allows acquiring similar skills:
Module 1Module 2LBIR1212 Probabilities and statistics (I)LBIR1315 Probability and statistics IIModule 3LINGE1113 ProbabilityFR
q2 30h+15h 4 credits
LINGE1214 Further StatisticsModule 4Programmation et informatiqueThe student must acquire the skills related to these three courses:
LINFO1101 Introduction to programmingLEPL1402 Informatics 2Other pre-requisite activitiesThe 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.
LINGE1222 Multivariate Statistical AnalysisLANGL1330 English intermediate level - 1st partEN
q1 or q2 20h 3 creditsTeacher(s):
> Stéphanie Brabant
> Charline Coduti (compensates Anne-Julie Toubeau)
> Estelle Dagneaux
> Jean-Luc Delghust
> Aurélie Deneumoustier
> Fanny Desterbecq
> Marie Duelz
> Claudine Grommersch
> Sandrine Mulkers (coord.)
> Yannick Paquin (compensates Anne-Julie Toubeau)
> Marc Piwnik (coord.)
> Françoise Stas
Stéphanie Brabant
Other EU to be determined with the Study AdvisorDepending 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.