Official title: Master in Statistics for Smart Data


Training duration

2 years

Average academic cost found without insurance

6 000€


Accreditation Provider: DGESIP

Prerequisite of the training

Bachelor (or equivalent)

Formation type

  • Initial education


  • Artificial Intelligence, Mathematics (Applied), Big data, Statistics

M1 section is taught by

M2 section is taught by

  • ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information

Training description

There is no M1 for this program at ENSAI. Students may complete the first year of the Master’s degree (ou 4th year of higher education) at any university and apply for this program.

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.

The structure of this all-English Master's program is the following: 1 semester of coursework at ENSAI, followed by a four to six-month paid internship in France or abroad within the professional world or academia/research laboratories. A one-month French language program precedes the start of the program.
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
ENSAI’s program goes beyond Big Data; it has shifted its emphasis to Smart Data, thus meeting the vital challenge of smart sensing and smart processing of the plethora of data available. Smart Data focuses on revealing the Value and Veracity from the Volume, Variety and Velocity of Big Data.
Thanks to ENSAI’s renowned expertise in Data Science and its innovative approach in training specialists to process and analyze data, strong links have been built with the professional world and graduates are highly sought after.

Scolarships (Subject to the school offer)

Year 2:

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 allocation). 2) Gustave Eiffel scholarships from the French government 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) Foreign students often receive scholarships from their home country and/or sending institution.

Master in Statistics for Smart Data

Language level prerequisites

The minimum language level required, namely : B2 in English will be verified no later than end of April, 2022 and will be verified with a skype interview. (to be confirmed in your academic offer)

Course language

Only English

Language requirement to graduate

English B2

M1 Part

M1 website



Not proposed by this institute

Average academic cost found without insurance



Credits Hours Language
No M1 is offered at ENSAI 0.00 0.00 English


Credits Hours Language
No M1 is offered at ENSAI 0.00 0.00 English

M2 Part


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

Average academic cost found without insurance



Credits Hours Language
Non-homogeneous Markov Chains 2.00 24.00 English
Graphical Networks & Dynamic Networks 2.00 24.00 English
Dynamic Data Visualization 1.00 12.00 English
Machine Learning: Features Selection 2.00 24.00 English
Deep Learning 2.00 24.00 English
Parallel Computing with R & Python 1.00 12.00 English
Smart Sensing: Foundations 2.00 24.00 English
Applications of Smart Sensing 3.00 36.00 English
High-Dimensional Time Series 2.50 30.00 English
Functional Data: Applications to Smart Grids 2.50 30.00 English
IT Tools 1: Spark, NoSQL, Haadoop 2.50 30.00 English
IT Tools 2: GNU Linux, Shell Scripting, Cloud Computing) 2.50 30.00 English
Energy Transitions: Quantitative Aspects 1.00 12.00 English
Smart Data Project 2.00 24.00 English
Conferences 2.00 24.00 English


Credits Hours Language
Internship 30.00 English