Address: 5 avenue Henry Le Chatelier, 91120 Palaiseau, Île-de-France, France
Data Science & Statistics, Quantitative Economics & Finance, Actuarial Science
Founded in 1942, ENSAE Paris is a prestigious public grande école specializing in economics, statistics, data science, and finance. It is part of Institut Polytechnique de Paris and GENES. While economics/finance are prominent, its strong quantitative/data science core is relevant.
ENSAE Paris, the École nationale de la statistique et de l’administration économique, was created in 1942 to train the statisticians and economists of the French state, at a time when the production of national statistics became a public priority. It has been associated with INSEE, the national statistics institute, from the start, and it trains many of the civil servants who work there. Since 2017 its home has been the Plateau de Saclay, in Palaiseau, and it has about 700 students and 200 staff.
The school belongs to Institut Polytechnique de Paris, with the École polytechnique and Télécom Paris, and to the GENES group, which also includes the national school of statistics and information analysis, ENSAI. The engineering degree combines mathematics, statistics, economics, finance, computer science and data science, with a strong emphasis on modelling and quantitative methods. A first-year core is followed by tracks in economics and public policy, finance and insurance, or data science and machine learning.
Research is carried out at CREST, the Centre de recherche en économie et statistique, a joint laboratory with the CNRS and the École polytechnique. It works on econometrics, microeconomics, macroeconomics, financial mathematics, statistical learning and public policy, and its researchers contribute to national debates on topics such as taxation, labour and inequality. Several alumni have led public bodies, financial institutions, think tanks and technology firms.
Students enter through competitive exams after preparatory classes or through other paths, including programmes at master’s level that attract international applicants. Courses are mainly in French, and the master’s programmes include options in English. Some students have the status of paid trainees of the state. Check the current tariff for fees, which differ by status.
Palaiseau is about 25 kilometres south of Paris, reachable by the RER B and by bus. Housing is available near the campus. The school is small, which allows close contact with professors, and graduates are in demand in banks, insurance companies, consulting firms and public institutions.
Domains taught:
AI and Machine Learning, Data Analytics, Engineering, Mathematics, Project Management, Quality, Software Engineering, Statistics
Diplomas*:
Doctor, Master
Status:
Public
Partnerships:
Part of Institut Polytechnique de Paris and GENES. Partnerships with INSEE, financial institutions, and research centers.
Research:
Machine learning and statistical modeling, Econometrics and quantitative finance, Actuarial science and risk management, Data science for social sciences.
Famous Alumnis:
Jean-Claude Trichet, Patrick Artus
International recognition**:
Number of staff***:
200
Current students***:
700
Foreign students***:
30%
Cumulative former students***:
7000
Pros
Leading institution for statistics, data science, and quantitative economics, excellent career prospects in data-driven fields.
Cons
Highly quantitative and demanding, strong focus on economics and finance alongside data science.
Best For
Elite grande école for statistics, data science, quantitative economics, and finance.
In terms of fields, ENSAI is the nearest neighbour, with quality, project management and statistics again at the core; it is slightly smaller, at about 500 students against roughly 700 here, and is also a public institution.
Another national school that puts quality, project management and statistics at the centre is Université Paris Dauphine - PSL, and it is also a public institution and also offers bachelor's and mba programmes.
Both schools build their programmes around quality, project management and statistics, and Télécom Paris is the one to compare first within the country, since it is also a public institution and also offers graduate certificate programmes.
Where this institution leans on quality, project management and statistics, ENSIIE does the same, and it covers ergonomics and UX and signal processing as well, and has almost the same size, at about 700 students.
Both schools build their programmes around quality, project management and mathematics, and Chimie ParisTech - PSL is the one to compare first within the country, since it covers nanotechnologies and microbiology and genetics as well, and is slightly smaller, at about 500 students against roughly 700 here.
Most Similar Schools Abroad
Looking internationally, Gwangju Institute of Science and Technology in South Korea is where AI and machine learning and engineering get a comparable treatment; it covers physics as well, and is slightly larger, at about 900 students against roughly 700 here.
CUNEF Universidad, in Spain, takes a similar route through statistics, mathematics and data analytics; it is slightly smaller, at about 600 students against roughly 700 here, and is private, whereas this institution is public.
The closest overseas parallel on quality, project management and statistics is Shiga University, located in Japan; it covers logistics and transportations as well, and is far larger, with about 5,000 students against roughly 700 here.
Abroad, Facultat de Matemàtiques i Informàtica (Spain) covers statistics, mathematics and data analytics in a comparable way, and it also offers bachelor's programmes and covers computer vision as well.
Seen from abroad, National Laboratory for Scientific Computing (Brazil) is a close relative on subject matter, since mathematics, data analytics and software engineering are central to both, and it is also a public institution and runs the same master's and doctoral programmes.
*our own picks (our criteria: a mix between domains, diploma delivered, reputation and size), but obviously too many criteria for an apple-to-apple ranking.
FAQ
Where is ENSAE Paris?
On the Plateau de Saclay, in Palaiseau, south of Paris, since 2017.
When was it created?
In 1942.
How large is it?
About 700 students and 200 staff.
What was it created for?
To train the statisticians and economists of the French state.
What is INSEE?
The French national statistics institute, with which the school has been associated since the start.
Which groups does it belong to?
Institut Polytechnique de Paris and GENES.
What is CREST?
The Centre de recherche en économie et statistique, a joint laboratory with the CNRS and the École polytechnique.
What tracks does it offer?
Economics and public policy, finance and insurance, or data science and machine learning.
How do students enter?
Through competitive exams after preparatory classes or through other paths, including master's programmes for international applicants.
What does it cost?
Fees differ by status. Check the current tariff.
Artificial Intelligence (AI), Machine Learning, Predictive Maintenance Algorithms, Quality Management, Statistical Analysis, Statistical Process Control (SPC)
*: without any judgment of the quality, readers are strongly encouraged to check the official status of the exact diploma envisioned as many universities have a mix of recognized diplomas, not recognized, “co-delivered,” “equivalent to”, “some pathways” (…), can change over time, and can be named differently from one country to another.
**: international reputation: is based on some criteria that do not mean much in some domains or specialties (number of researchers, of scientific publications, on budget ….). Our approximate 1-4 scale:
known locally
known locally and internationally
is listed in some of the world university’s classifications
is listed among the 20 firsts in some of world university’s classifications
***: estimated numbers, based on online sources and/or feedbacks. The numbers can vary greatly depending on school organisation (ex.: school is part of a group, it employs internal staff or external consultants or has public staff without employing them …). Refer to all the links provided to have accurate and updated numbers.
Innovation.world is not affiliated with this school/university. We recommend to contact them directly on their website for any special request.
To complete or correct any information here or share your opinion, please comment below or > send us a direct message <.
