MS Data Science
This training is at the Bac+6 level, and is intended for candidates who already have a Bac+5 degree (find out more about admission).
The targeted professions are those of data scientist, statistical analyst, chief data officer or business analyst.
The data scientist is a specialist in the digital economy and the processing of large data files, capable of inventing new uses and extracting value from them. He or she is at the crossroads of computer science and statistical analysis and has a very high level of scientific expertise that enables him or her to help make decisions in many fields: online advertising targeting, e-commerce marketing or more traditional customer relations, public policy evaluation, high-frequency trading, imaging, academic research, etc. This versatile profile can lead to careers as experts as well as to decision-making or management positions in companies.
Data scientist profiles are now actively sought after in France and abroad, in start-ups as well as in large groups for which the use of customer data is strategic: internet (Google, Facebook, Deezer, etc.), customer data from banks and insurance companies (Crédit Agricole, Axa, etc.) or large companies (SNCF, EDF, etc.). Research positions are also available in institutions responsible for evaluating the effectiveness of public policies or studying the behavior of economic agents (INSEE, Ministries, social security funds, UNEDIC, OFCE, Banque de France, Institute of Public Policies, CRÉDOC, OECD, World Bank, European institutions, IMF, etc.).
The courses listed below correspond to the provisional pedagogical model for the year 2021-22, which may be subject to change.
- 420 hours of teaching
- A 4 to 6 month internship at the end of the program
The program of the Specialized Master® Data Science is based on the three pillars that characterize the profession of data scientist and that are in demand on the job market:
- a theoretical and methodological pillar that covers models and methods of machine learning, Bayesian inference, high-dimensional statistics, network analysis ;
- a technological/software pillar that includes programming languages and their machine learning libraries (Python, Matlab, etc.), statistical software (R, Stata, etc.), database management tools (SQL, NoSQL) and the creation of parallelized/distributed applications for Big Data processing (Hadoop, Mapreduce, etc.);
- a pillar of domain expertise, in particular in quantitative marketing, finance, economics.
Professional conferences complete these courses, where external speakers from the professional world address current topics and/or practical aspects of the data scientist's job.
The training starts at the end of August with a 5-week full-time harmonization block. The courses are then grouped on 3 days of the week from October to mid-May, followed by the end-of-study internship from May to end of September. It is possible to start the internship early, alternating days in the company (Mondays and Thursdays) and days of classes; except for review and exam weeks.
Approximately 30% of the courses are taught by permanent teachers, 25% by external teachers and 45% by professionals (Google, Facebook, Microsoft, Crédit Agricole, Insee, etc.).
- Time series
- Introduction to R
- Introduction to Python
- Introduction to statistical learning
- Mathematical Statistics
- Integration in MS: the foundations of the link
- Bases de données
- Big data et droit des données
- Apprentissage Statistique appliqué
- Analyse financière et stratégie d’entreprise
- Blockchain: Bitcoin and Smart-Contracts
- Dynamic pricing and revenue management
- Eléments logiciels pour le traitement de données massives – Hadoop
- Entrepreneuriat 1
- Machine learning avec Python
- Modeling and managing energy risks
- Optimisation avancée
- Social Science Genetics
- Statistical Methods of Econometrics
Par ailleurs, les étudiants intéressés par le Business data challenge (attention, capacité d’accueil restreinte) doivent le choisir de manière définitive en septembre uniquement, cet enseignement ayant lieu sur l’ensemble de l’année (ECTS comptant sur le 2e semestre).
- Deep Learning: Models and Optimization
- Seminar in Quantitative Marketing ou Data Storytelling
- Compétences relationnelles et codes de l’entreprise
- Artificial intelligence in insurance and actuarial studies
- Bootstrap and Resampling Methods
- Business data challenge (capacité d’accueil restreinte et enseignement sur l’ensemble de l’année, à choisir de manière définitive en septembre uniquement)
- Cloud computing
- Entrepreneuriat 2 (pré-requis : avoir suivi Entrepreneuriat 1)
- Entrepreneuriat digital
- Fabrication d’enquêtes
- Histoire et épistémiologie de la statistique
- Machine learning for finance
- Machine Learning for Natural Language Processing
- Optimal Transport: Theory, Computations, Statistics, and ML Applications
- Online learning and aggregation
- Programmation GPU
- Reinforcement learning
- Science des réseaux sociaux et économiques
- Sociological perspectives on inequality
- Sociologie des pratiques culturelles
- Sociology of health and illness
- Statistique 3
- Statistique bayésienne
- The Advanced Master's program ends with a 4 to 6 month internship starting in mid-May (minimum 16 weeks for the Specialized Master's program, and 6 months to present a thesis to the Institute of Actuaries). This internship can be started in advance on Mondays and Thursdays, as a part-time internship, in agreement with the ENSAE internship service. In this case, it is advisable to remain vigilant about the workload; it is not advisable to start this part-time internship before February for the MS-Actuarial Science, as the requirements for validation of the curriculum for the Institute of Actuaries require significant personal work throughout the year.
Who is the MS Data Science for?
This program is intended for people who have a solid mathematical background (particularly in applied mathematics, statistics and probability).
The standard recruitment corresponds to students or professionals with a Bac+5 (Master 2 or equivalent) who want to acquire a complementary training allowing them to be competitive on the job market. It is recommended to have a M1 or M2 level in applied mathematics, statistics or mathematical finance, or an engineering or business school diploma with significant mathematical or statistical content.
A harmonization block at the beginning of the program (end of August to beginning of October) aims to consolidate the knowledge base necessary to follow the courses shared with the third year of the engineering program.
You can find more information on the course of study here. If you are interested in a course with more internship periods, we recommend the MS Data science for customer knowledge of our partner school, ENSAI.
The cost of the training is fixed at :
- 14 000€ for professionals, companies or administrations;
- 9 500€ for students continuing their studies or job seekers.