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M2 Data Science

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  • Places available
    20
  • Language(s) of instruction
    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
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.

Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
[AI] OPT 10: IMAGE INDEXING AND UNDERSTANDING 2.5 15 6
[AI] OPT 11: DEEP LEARNING FOR NLP 2.5 18 3
[AI] OPT 12: INFORMATION EXTRACTION FROM DOCUMENTS TO INTERFACES 2.5 10.5 10.5
[AI] OPT 13: Theorie de l'information 2.5 10.5 10.5 0 0
[AI] OPT1 : GRAPHICAL MODELS 2.5 15 6
[AI] OPT14:MULTILINGUAL NATURAL LANGUAGE PROCESSING 2.5 21
[AI] OPT2: IMAGE PROCESSING 2.5 21
[AI] OPT3 : REINFORCEMENT LEARNING 2.5 15 6
[AI] OPT4: DEEP LEARNING 2.5 10.5 10.5
[AI] OPT5 : VOICE RECOGNITION AND AUTOMATIC LANGUAGE PROCESSING 2.5 21
[AI] OPT6: LEARNING THEORY AND ADVANCED MACHINE LEARNING 2.5 21
[AI] OPT7: ADVANCED OPTIMIZATION 2.5 12 4.5 4.5
[AI] OPT8: GAME THEORY 2.5 12 4.5 4.5
[AI] OPT9: DATA CAMP 2.5 10 15
[AI] PRE1: APPLIED STATISTICS 2.5 10.5 10.5
[AI] PRE2: MATHEMATICS FOR DATA SCIENCE 2.5 12 4.5 4.5
[AI] PRE3: DATACOMP 1 2.5 12 9
[AI] PRE4: SCIENTIFIC PROGRAMMING 2.5 9 12
[AI] TC0 : Introduction to Machine Learning 2.5 15 6
[AI] TC1: MACHINE LEARNING 2.5 15 6
[AI] TC2: OPTIMIZATION 2.5 12 4.5 4.5
[AI] TC3: INFORMATION RETRIEVAL 2.5 9 12
[AI] TC4: Probabilistic Generative Models 2.5 16.5 4.5
[AI] TC5: SIGNAL PROCESSING 2.5 24
[AI] TC6: DATACOMP 2 2.5 12 9
[ANO] Blockchain 2.5
[ANO] Evaluation de performances 2.5
[ANO] Internet of Things 2.5 21
[ANO] Optimisation dans les graphes 2.5 21
[ANO] Optimisation discrète non linéaire 2.5 21
[ANO] Optimisation multi-objectifs 2.5 21
[ANO] MPI programming 2.5
[ANO] Programmation système et réseaux 2.5 21
[ANO] Réseaux mobiles 2.5 21
[ANO] Réseaux sans fil 2.5 21
[ANO] Tests fonctionnels de protocoles 2.5 21
[ANO] Théorie des jeux 2.5 21
[ANO] Virtualisation et cloud 2.5
[DS] Algorithms for Data Science 2.5 12 9
[DS] Bases de données avancées I : Optimisation 2.5 9 8 4
[DS] Bases de données avancées II : Transactions 2.5 9 8 4
[DS] Distributed Systems for Massive Data Management 2.5 12 0 9
[DS] Intelligence Artificielle, Logique et Contraintes 2.5 10.5 10.5
[DS] Intelligence Artificielle, Logique et Contraintes : Projet 2.5 10.5 10.5
[DS] Knowledge Discovery in Graph Data 2.5 12 6 3
[DS] Semantic Web and Ontologies 2.5 12 9
[DS] Social and Graph Data Management 2.5 12 9
[DS] Social and Graph Data Management 2.5 12 9
[DS] Web of Data: Principles and Applications 2.5 12 3 6
[HCI] Advanced Design of Interactive Systems 2.5
[HCI] Advanced Immersive Interactions 2.5 21
[HCI] Advanced Immersive Interactions - Project 2.5
[HCI] Advanced Programming of Interactive Systems 1 2.5
[HCI] Advanced Programming of Interactive Systems 2 2.5
[HCI] Career Seminar - Level 1 2.5
[HCI] Career Seminar - Level 1 : Project 2.5 21
[HCI] Career Seminar - Level 2 2.5
[HCI] Career Seminar - Level 2 project 2.5
[HCI] Creative Design 2.5
[HCI] Creative Design : Project 2.5
[HCI] Design of Interactive Systems 2.5
[HCI] Design project - Level 1 2.5 21
[HCI] Design project - Level 1 : Project 2.5 21
[HCI] Design project - Level 2 2.5 21
[HCI] Design project - Level 2 : Project 2.5 21
[HCI] Digital Fabrication 2.5
[HCI] Digital fabrication : Project 2.5
[HCI] Evaluation of Interactive Systems 2.5
[HCI] Experimental Design and Analysis 2.5
[HCI] Fundamental of Human-Computer Interaction 1 2.5
[HCI] Fundamental of Human-Computer Interaction 2 2.5
[HCI] Fundamental of situated computing 2.5
[HCI] Fundamentals of eXtended Reality 2.5
[HCI] Gestural and Mobile Interaction 2.5
[HCI] Groupware and Collaborative Work 2.5 21
[HCI] Groupware and Collaborative Work : Project 2.5 21
[HCI] Interactive Information Visualization 2.5
[HCI] Interactive Information Visualization : Project 2.5
[HCI] Interactive Machine Learning 2.5
[HCI] Interactive Machine Learning : Project 2.5
[HCI] Mixed Reality and Tangible Interaction 2.5 21
[HCI] Mixed Reality and Tangible Interaction - Project 2.5 21
[HCI] Programming of Interactive Systems 1 2.5
[HCI] Programming of Interactive Systems 2 2.5
[HCI] Serious games 2.5
[HCI] Serious games : project 2.5
[HCI] Studio Art Science 2.5 21
[HCI] Virtual Humans 2.5 21
[HCI] Virtual Humans : Project 2.5 21
[ISD] Algorithmique avancée 3 25
[ISD] Algorithmique distribuée 3 25
[ISD] Anglais 3 25
[ISD] Anglais 3 25
[ISD] Blockchain 3 25
[ISD] Cloud Computing 3 25
[ISD] Communication 3 25
[ISD] Data Lake 3 25
[ISD] Data Warehouse I 3 25
[ISD] Data Warehouse II 3 25
[ISD] Droit informatique 3 25
[ISD] Extraction et programmation statistique de l'information 3 25
[ISD] Introduction à l'apprentissage 3 25
[ISD] IoT (Internet des objets) 3 25
[ISD] langages Dynamiques 3 25
[ISD] Machine learning/Deep learning 3 25
[ISD] Mémoire 12 8
[ISD] Modèles Mathématiques 3 25
[ISD] Modélisation 3 25
[ISD] Optimisation 3 25
[ISD] outils pour la manipulation et l'extraction de données 3 25
[ISD] Politiques et concepts avancés en sécurité 3 25
[ISD] Probabilités/Statistiques 3 25
[ISD] Programmation système et réseau 3 25
[ISD] Projet étude de cas 3 25
[ISD] Projets tuteurés 6 25
[ISD] Rapport d'activité 6 5
[ISD] Représentation des connaissances et visualisation 3 25
[ISD] Réseaux 3 25
[ISD] Réseaux sans fil 3 25
[ISD] sécurité 3 25
[ISD] Services et applications Web 3 25
[ISD] Test et Vérification 3 25
[ISD] Traitement automatique des langues 3 25
[ISD] Traitement distribué des données. 3 25
[QDCS] Algorithms in the nature 2.5 21
[QDCS] Robust distributed algorithms 2.5 21
[QDCS] Parallel algorithms 2.5 12 6 3
[QDCS] Auto-stabilisation 2.5 21
[QDCS] Big Data 2.5 12 3 8
[M1 QDCS] High performance computing 2.5 12 9
[QDCS] [M2 QDCS] Recent trends in parallel, distributed, and quantum computing 2.5 21
[QDCS] Initiation to quantum algorithms and programming 2.5 21
[M1 QDCS] Game, learning, and optimisation of 2.5 21
complex systems 2.5 21
[QDCS] Stochastic optimisation 2.5 21
[QDCS] Ordonnancement et systèmes d'exécution 2.5 21
[QDCS] Advanced C++ programming 2.5 9 0 12
[QDCS] GPU programming 2.5 12 9
[QDCS] Programmation orientée objet 2.5 11 10
[SOFT] Soft skills - 1A (Langue) 2.5 21
[SOFT] Soft skills - 1B (Langue) 2.5 100
[SOFT] Soft skills - 2 (Communication) 2.5 21
[SOFT] Soft skills - 3 (Formation à la vie de l'entreprise - Initiation) 2.5 21
[SOFT] Soft skills - 4 Innovation et Entreprenariat 2.5 21
[SOFT] Soft skills - 5 Innovation et Entreprenariat avancé 2.5 21
[SOFT] Soft skills - Seminars (Fairness in Data Science) 2.5 20
[SOFT] Soft skills - Seminars B 2.5
[SOFT] Soft skills - Summer school 2.5 21
[SOFT] Soft skills - Transversal Project A 2.5 7 7 7
[SOFT] Soft skills - Transversal Project B 2.5 7 7 7
EIT - Business Development Lab 1 4
EIT - Business Development Lab 2 5
EIT - Innovation & Entrepreneurship Advanced 1 2.5 21
EIT - Innovation & Entrepreneurship Advanced 2 2.5
EIT - Innovation & Entrepreneurship Study 1 3 21
EIT - Innovation & Entrepreneurship Study 2 3
EIT - Innovation and Entrepreneurship Basics 1 3
EIT - Innovation and Entrepreneurship Basics 2 3
EIT - Summer School 4
French Language and Culture 1 2 30
French Language and Culture 2 2 21
Long internship 30
TER Stage 10
Modalités de candidatures
Application period
From 01/03/2024 to 30/04/2024
Compulsory supporting documents
  • 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)
  • Curriculum Vitae.

  • Selection sheet completed.

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

  • Supporting documents :
    - Residence permit stating the country of residence of the first country
    - Or receipt of request stating the country of first asylum
    - Or document from the UNHCR granting refugee status
    - Or receipt of refugee status request delivered in France
    - Or residence permit stating the refugee status delivered in France
    - Or document stating subsidiary protection in France or abroad
    - Or document stating temporary protection in France or abroad.

Contact(s)
Course manager(s)
Administrative office
Admission