M2 Data Science

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  • Places available
    40
  • Language(s) of instruction
    French, English
Présentation
Objectives

This master allows students to gain strong theoretical and applied skills to be able to successfully manage today's mass of data of the real world. Specifically, students will be able to gain specialized skills in handling modern database systems and the algorithms used for data analysis at huge scales. Indeed, the main focus will be on how to handle data and knowledge that is large, but also heterogeneous and in some cases unreliable, via algorithms and systems spanning the areas of data science and engineering, data mining, and machine learning.

This programme is offered as an introductory course (hosting capacity: 15) and as apprenticeship training (work-study programme) (hosting capacity: 25)

  • Introductory course:

recruitment session from 01/05/2020 to 01/07/2020

  • Apprenticeship training (work-study):

recruitment session from 01/04/2020 to 01/07/2020

This programme is offered as an introductory course (hosting capacity: 15) and in work-study programme (hosting capacity: 25)

Location
ORSAY
GIF SUR YVETTE
PALAISEAU
Course Prerequisites

Holders of a DataScience or Artificial Intelligence M1 from Université Paris-Saclay or an equivalent qualification for students from outside Paris-Saclay. For the work-study track, students must have obtained the Data Science M1 via a work-study programme. Contracts with the companies last for two years. Prospective students are expected to have completed a minimum of 4 years of tertiary studies in Computer Science, either via an M1 in Paris-Saclay or via an equivalent diploma for foreign students.

Skills
  • Acquire the theoretical foundations belonging to various types of data science approaches.

  • Design and develop systems for the management of big data and heterogeneous data.

  • Build, evaluate and interpret analysis and learning models taking into account the nature of the data.

  • Have a good command of the learning methodology, from raw data (from scratch) to evaluation.

Post-graduate profile

Data scientist/engineer, designer/developer of the architecture of big data management and analysis, applications manager, applications integrator, data architect, R&D engineer.

Career prospects

This study course enables students to easily join the industrial world in companies that develop innovative software, in start-ups or in the R&D departments of large companies. It also paves the way to a doctoral programme, preparing students to write a thesis, either by joining a research organisation (public or private) or an R&D department in a company
This study path targets professions such as Data Analyst, Database administrator, Big Data manager. Big Data, Application Manager, Application Integrator, Data Architect, Big Data Application Designer/Developer, Research and Development Engineer.

Collaboration(s)
Laboratories

Laboratoire de recherche en informatique
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
Laboratoire Spécification et Vérification.

Programme

Le parcours-type Data Science peut être validé selon deux voies de formation :
- La voie par alternance (ISD)
- La voie initiale (DS)

Toutes les UEs listées ci-dessous devront être validées au cours du parcours (M1 / M2): il est requis d'acquerir 60 ECTS par niveau, pour un total de 120 ECTS à l'issue des deux années.

** Pour valider la voie initiale DS (M1 & M2), les étudiants devront valider toutes les UE intitulées [DS].
À ces UE s'ajoutent :
- [AI] TC1 : Machine Learning
- [AI] TC2 : Optimization
- [AI] TC6 : Datacomp 2
- Visual Analytics (UE du M2 BDMA)

Pour atteindre 120 crédit ECTS, chaque étudiant devra compléter son parcours avec 4 UE dont l'intitulé est Soft skills - xxxx, un TER-Stage (en M1), un stage long (au second semestre du M2) ainsi qu'un libre choix de cours d'autres parcours-types pour compléter les 120 crédits ECTS.

** Pour valider la voie par alternance, les étudiants devront suivre toutes les UE [ISD] pour un total de 120 ECTS. Les cours dans cette voie sont dispensés en français.

Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
TER Stage 10
Stage long 30
French Language and Culture 2 2 21
French Language and Culture 1 2 30
EIT - Summer School 4
EIT - Innovation and Entrepreneurship Basics 2 3
EIT - Innovation and Entrepreneurship Basics 1 3
EIT - Innovation & Entrepreneurship Study 2 3
EIT - Innovation & Entrepreneurship Study 1 3 21
EIT - Innovation & Entrepreneurship Advanced 2 2.5
EIT - Innovation & Entrepreneurship Advanced 1 2.5 21
EIT - Business Development Lab 2 5
EIT - Business Development Lab 1 4
[SOFT] Soft skills - Transversal Project B 2.5 7 7 7
[SOFT] Soft skills - Transversal Project A 2.5 7 7 7
[SOFT] Soft skills - Summer school 2.5 21
[SOFT] Soft skills - Seminars B 2.5
[SOFT] Soft skills - Seminars (Fairness in Data Science) 2.5 20
[SOFT] Soft skills - 5 Innovation et Entreprenariat avancé 2.5 21
[SOFT] Soft skills - 4 Innovation et Entreprenariat 2.5 21
[SOFT] Soft skills - 3 (Formation à la vie de l'entreprise - Initiation) 2.5 21
[SOFT] Soft skills - 2 (Communication) 2.5 21
[SOFT] Soft skills - 1B (Langue) 2.5 100
[SOFT] Soft skills - 1A (Langue) 2.5 21
[PDCS] Programmation orientée objet 2.5 11 10
[PDCS] Programmation GPU 2.5 12 9
[PDCS] Programmation avancée C++ 2.5 9 0 12
[PDCS] Ordonnancement et systèmes d'exécution 2.5 21
[PDCS] Optimisation stochastique 2.5 21
[PDCS] Modélisation et optimisation des systèmes discrets 2.5 21
[PDCS] Jeux, apprentissage et optimisation des systèmes complexes 2.5 21
[PDCS] Initiation au calcul quantique 2.5 21
[PDCS] Frontières du calcul parallèle et distribué 2.5 21
[PDCS] Calcul Haute Performance 2.5 12 9
[PDCS] Big Data 2.5 12 3 8
[PDCS] Auto-stabilisation 2.5 21
[PDCS] Algorithmique parallèle 2.5 12 6 3
[PDCS] Algorithmes distribués robustes 2.5 21
[PDCS] Algorithmes de la nature 2.5 21
[ISD] Traitement distribué des données. 3 25
[ISD] Traitement automatique des langues 3 25
[ISD] Test et Vérification 3 25
[ISD] Services et applications Web 3 25
[ISD] sécurité 3 25
[ISD] Réseaux sans fil 3 25
[ISD] Réseaux 3 25
[ISD] Représentation des connaissances et visualisation 3 25
[ISD] Rapport d'activité 6 5
[ISD] Projets tuteurés 6 25
[ISD] Projet étude de cas 3 25
[ISD] Programmation système et réseau 3 25
[ISD] Probabilités/Statistiques 3 25
[ISD] Politiques et concepts avancés en sécurité 3 25
[ISD] outils pour la manipulation et l'extraction de données 3 25
[ISD] Optimisation 3 25
[ISD] Modélisation 3 25
[ISD] Modèles Mathématiques 3 25
[ISD] Mémoire 12 8
[ISD] Machine learning/Deep learning 3 25
[ISD] langages Dynamiques 3 25
[ISD] IoT (Internet des objets) 3 25
[ISD] Introduction à l'apprentissage 3 25
[ISD] Extraction et programmation statistique de l'information 3 25
[ISD] Droit informatique 3 25
[ISD] Data Warehouse II 3 25
[ISD] Data Warehouse I 3 25
[ISD] Data Lake 3 25
[ISD] Communication 3 25
[ISD] Cloud Computing 3 25
[ISD] Blockchain 3 25
[ISD] Anglais 3 25
[ISD] Anglais 3 25
[ISD] Algorithmique distribuée 3 25
[ISD] Algorithmique avancée 3 25
[HCI] Virtual Humans : Project 2.5 21
[HCI] Virtual Humans 2.5 21
[HCI] Studio Art Science 2.5 21
[HCI] Serious games : project 2.5
[HCI] Serious games 2.5
[HCI] Programming of Interactive Systems 2 2.5
[HCI] Programming of Interactive Systems 1 2.5
[HCI] Mixed Reality and Tangible Interaction - Project 2.5 21
[HCI] Mixed Reality and Tangible Interaction 2.5 21
[HCI] Interactive Machine Learning : Project 2.5
[HCI] Interactive Machine Learning 2.5
[HCI] Interactive Information Visualization : Project 2.5
[HCI] Interactive Information Visualization 2.5
[HCI] Groupware and Collaborative Work : Project 2.5 21
[HCI] Groupware and Collaborative Work 2.5 21
[HCI] Gestural and Mobile Interaction 2.5
[HCI] Fundamentals of eXtended Reality 2.5
[HCI] Fundamental of situated computing 2.5
[HCI] Fundamental of Human-Computer Interaction 2 2.5
[HCI] Fundamental of Human-Computer Interaction 1 2.5
[HCI] Experimental Design and Analysis 2.5
[HCI] Evaluation of Interactive Systems 2.5
[HCI] Digital fabrication : Project 2.5
[HCI] Digital Fabrication 2.5
[HCI] Design project - Level 2 : Project 2.5 21
[HCI] Design project - Level 2 2.5 21
[HCI] Design project - Level 1 : Project 2.5 21
[HCI] Design project - Level 1 2.5 21
[HCI] Design of Interactive Systems 2.5
[HCI] Creative Design : Project 2.5
[HCI] Creative Design 2.5
[HCI] Career Seminar - Level 2 project 2.5
[HCI] Career Seminar - Level 2 2.5
[HCI] Career Seminar - Level 1 : Project 2.5 21
[HCI] Career Seminar - Level 1 2.5
[HCI] Advanced Programming of Interactive Systems 2 2.5
[HCI] Advanced Programming of Interactive Systems 1 2.5
[HCI] Advanced Immersive Interactions - Project 2.5
[HCI] Advanced Immersive Interactions 2.5 21
[HCI] Advanced Design of Interactive Systems 2.5
[DS] Social and Graph Data Management 2.5 12 9
[DS] Semantic Web and Ontologies 2.5 12 9
[DS] Knowledge Discovery in Graph Data 2.5 12 6 3
[DS] Intelligence Artificielle, Logique et Contraintes : Projet 2.5 10.5 10.5
[DS] Intelligence Artificielle, Logique et Contraintes 2.5 10.5 10.5
[DS] Distributed Systems for Massive Data Management 2.5 12 0 9
[DS] Data Science Project 2.5 3 18
[DS] Bases de données avancées II : Transactions 2.5 9 8 4
[DS] Bases de données avancées I : Optimisation 2.5 9 8 4
[DS] Algorithms for Data Science 2.5 12 9
[ANO] Virtualisation et cloud 2.5
[ANO] Théorie des jeux 2.5 21
[ANO] Tests fonctionnels de protocoles 2.5 21
[ANO] Réseaux sans fil 2.5 21
[ANO] Réseaux mobiles 2.5 21
[ANO] Programmation système et réseaux 2.5 21
[ANO] Programmation MPI 2.5
[ANO] Optimisation multi-objectifs 2.5 21
[ANO] Optimisation discrète non linéaire 2.5 21
[ANO] Optimisation dans les graphes 2.5 21
[ANO] Internet of Things 2.5 21
[ANO] Evaluation de performances 2.5
[ANO] Blockchain 2.5
[AI] TC6: DATACOMP 2 2.5 12 9
[AI] TC5: SIGNAL PROCESSING 2.5 24
[AI] TC4: Probabilistic Generative Models 2.5 16.5 4.5
[AI] TC3: INFORMATION RETRIEVAL 2.5 9 12
[AI] TC2: OPTIMIZATION 2.5 12 4.5 4.5
[AI] TC1: MACHINE LEARNING 2.5 15 6
[AI] TC0 : Introduction to Machine Learning 2.5 15 6
[AI] PRE4: SCIENTIFIC PROGRAMMING 2.5 9 12
[AI] PRE3: DATACOMP 1 2.5 12 9
[AI] PRE2: MATHEMATICS FOR DATA SCIENCE 2.5 12 4.5 4.5
[AI] PRE1: APPLIED STATISTICS 2.5 10.5 10.5
[AI] OPT9: DATA CAMP 2.5 10 15
[AI] OPT8: GAME THEORY 2.5 12 4.5 4.5
[AI] OPT7: ADVANCED OPTIMIZATION 2.5 12 4.5 4.5
[AI] OPT6: LEARNING THEORY AND ADVANCED MACHINE LEARNING 2.5 21
[AI] OPT5 : VOICE RECOGNITION AND AUTOMATIC LANGUAGE PROCESSING 2.5 21
[AI] OPT4: DEEP LEARNING 2.5 10.5 10.5
[AI] OPT3 : REINFORCEMENT LEARNING 2.5 15 6
[AI] OPT2: IMAGE PROCESSING 2.5 21
[AI] OPT14:MULTILINGUAL NATURAL LANGUAGE PROCESSING 2.5 21
[AI] OPT1 : GRAPHICAL MODELS 2.5 15 6
[AI] OPT 13: Theorie de l'information 2.5 10.5 10.5 0 0
[AI] OPT 12: INFORMATION EXTRACTION FROM DOCUMENTS TO INTERFACES 2.5 10.5 10.5
[AI] OPT 11: DEEP LEARNING FOR NLP 2.5 18 3
[AI] OPT 10: IMAGE INDEXING AND UNDERSTANDING 2.5 15 6
Modalités de candidatures
Application period
From 01/05/2020 to 01/07/2020
Compulsory supporting documents
  • Curriculum Vitae.

  • Motivation letter.

  • All transcripts of the years / semesters validated since the high school diploma at the date of application.

  • Certificate of English level.

    (only for the M2 Data Science - initiale)
  • Selection sheet completed (to download on the training website).

Additional supporting documents
  • VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).

Contact(s)
Course manager(s)
Silviu Maniu - silviu.maniu@lri.fr
Admission