Master i ENSAI Data (Big Data), Statistics

Computer Science Track

Titre officiel
Master international - Traitement statistique de données volumineuses (DNM)

Site web de l'école

Pré-requis de la formation
Bachelor (minimum)

Type de formation
Formation initiale

Langue d'enseignement
Option M1 M2
Computer Science Track Anglais Anglais

La partie M1 est enseignée par
Non enseigné mais soumis à reconnaissance

La partie M2 est enseignée par
L'établissement : ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information

Mots clés
Data mining, Networks (Computer), Data Warehouse, Mathematics (Applied), Data (Big Data), Statistics
Partie M1


Coût académique moyen constaté hors assurance du M1

Description M1
THERE IS NO TAUGHT M1 YEAR FOR THIS PROGRAM; the courses given for M1 are ficticious and are merely presented to give candidates an idea of what equivalent background/profile they should ideally possess.

Ideal candidates applying to the M2 year should have completed a minimum of four years of higher education in the fields of Applied Mathematics, Computer Science, or Statistics. In their previous/current studies, candidates would have ideally already acquired preliminary knowledge in as many of the following areas as possible: fundamental mathematical knowledge of statistics, probability, numerical methods, statistical software, databases, object-oriented programming, inferential statistics and tests, linear models, web technologies, and application projects.

Bourses M1

M1 url

Période 1
September - December Langue Heures* Crédits*
Note: tout ou partie du M1-Période 1 sera remplacée par un package "n+i" spécialement conçu pour les élèves étrangers. Voir le Package d'Intégration Méthodologique (PIM) pour plus d'informations.
Introduction au systeme SAS en 20.00 2.00

Introduction a R en 20.00 2.00

Optimisation et methodes numeriques en 25.00 2.00

Option : Computer Science Track
Algorithmique en 15.00 2.00

Bases de donnees en 15.00 2.00

Projet en 15.00 1.00

Modelisation UML en 15.00 2.00

Programmation Java en 30.00 2.00

Projet Objet en 15.00 2.00

Période 2
January - May Langue Heures* Crédits*
Estimation et tests en 50.00 6.00

Inference statistique assistee par ordinateur en 40.00 6.00

End-of-First Year Internship (2 months between June and August, either in France or abroad) en 0.00 12.00

Option : Computer Science Track
Gestion de projet en 20.00 2.00

Technologies web en 40.00 4.00

Projet informatique en 40.00 5.00

* Les heures de cours indiquées peuvent être soumis à de légères variations.
* Plus d'informations sur les crédits ECTS.
Partie M2

ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information
Campus de Ker Lann
Rue Blaise Pascal - BP 37203
35172 Bruz Cedex

Coût académique moyen constaté hors assurance du M2
8260 euros

Description M2
The structure of this all-English Master's program is the following: 2 semesters of coursework at ENSAI, followed by a five-month paid internship in France or abroad within the professional world or academia/research laboratories. A 2-month French program precedes the start of the program.

Concerning the 2 semesters of coursework, the program begins with Statistics and Computer Science courses tailored for students with different profiles, allowing for a diverse student body to acquire homogenous levels during the first semester. These take the form of the 2 initial tracks, and admitted students are placed in the proper track at the discretion of ENSAI. This does NOT result in a speciality at the end of the program, but these 2 tracks should be considered more as 'refresher courses.'

Throughout the program, students will:

- Learn the theoretical aspects and the practical skills needed to become a Data Scientist in order to meet the growing needs of a large variety of companies and organizations, such as retailers, manufacturers, financial markets, insurance companies, healthcare providers, or public administrations

- Acquire the necessary tools to handle and analyze massive amounts of heterogeneous data

- Master the statistical methods essential for rapidly extracting information from multiple datasets and the IT methods suitable for stocking the data

Ultimately, graduates will have learned to master the 3Vs of Big Data:

- Volume: To analyze large volumes of data using their strong background in machine learning, statistics, data mining, business intelligence and high-performance computing

- Velocity: To extrapolate reliable information rapidly from masses of data thanks to a sound understanding and mastery of cloud computing, highly-scalable data-storage paradigms (eg. NoSQL), and modern tools for massively distributed data processing, such as Hadoop and Pig

- Variety: To combine and interpret multiple data sources whilst extracting value from structured, unstructured, textual, and functional data

Bourses M2
Students following this Master's program are not eligible to apply for GENES' student grant based on financial need. However, foreign students through "n+i" may be eligible for the scholarships available from the "n+i" Network (Bni). Other than this, here are some other elements/possibilities to consider: 1) International students are entitled to an accommodation allowance (APL/ALS) from the French government to help with the cost of rent (approximately 180€/month). 2) Gustave Eiffel scholarships from the French government (BGF) for international students admitted to a degree awarding program and showing proof of outstanding academic performance (application deadline in December). 3) French Embassy scholarships program: For more information contact the local French Embassy. 4) When other foreign students (non-n+i students) come to ENSAI, other grants or scholarships they may receive come from their home country and/or sending institution (if they're studying abroad temporarily from another university or engineering school).

Période 1
September - December Langue Heures* Crédits*
Aggregation Methods in Statistics and Combinatorial Complexity en 20.00 2.00

Association Rules Mining en 10.00 1.00

Data Visualization en 10.00 1.00

Olap, Multidimensional Databases en 15.00 1.50

Big Data Databases en 15.00 1.50

NoSQL en 10.00 1.00

Penalized Regression en 25.00 2.50

Variable Selection Methods en 15.00 1.50

Unix (shell script) en 20.00 2.00

Parallelized Systems en 20.00 2.00

Courses for foreigners: Oral and/or Written French Language Courses (at CIREFE) (for foreign students with no existing French skills) fr 22.00 2.00

Intensive French Summer Program (July-August at CIREFE) (for foreign students with no existing French skills) fr 0.00 6.00

Option : Computer Science Track
Client - Server Architecture, JavaEE en 25.00 3.00

Cloud Computing en 10.00 1.00

JavaEE Project en 10.00 2.00

Computer Networks en 40.00 5.00

Période 2
January - September Langue Heures* Crédits*
Functional Data Analysis en 25.00 2.50

Text Mining, Image Analysis en 15.00 1.50

Compressive Sensing en 20.00 2.00

Parsimonious Representations en 20.00 2.00

Foundations of Big Data using MapReduce en 20.00 2.00

Hadoop Technologies (batch/real time processing), Storm, HD File System en 20.00 2.00

Programming with Big Data in R using Distributed Memory en 20.00 2.00

Statistical Libraries for Big Data (Mahout, SAS, HPA) en 20.00 2.00

Secure Pairing, Security Services against Piracy, Cryptography en 30.00 3.00

Privacy en 10.00 1.00

Big Data Project en 40.00 4.00

Courses for Foreigners: Oral and/or Written French Language Courses (at CIREFE) (for foreign students with no existing French skills) fr 22.00 2.00

End-of-Studies Internship (5 months from May-September, to be completed in France or abroad) en 0.00 25.00

* Les heures de cours indiquées peuvent être soumis à de légères variations.
* Plus d'informations sur les crédits ECTS.