M2 Econometrics Statistics | Econometrics and Data Science (EDS)

This is a programme in Data Science built on solid statistical and econometric foundations. Students will learn how to code and apply machine learning techniques as well as interpret and communicate the results of their scientific projects. This will enable them to contribute to the industrial and administrative decision-making process by providing relevant and robust input. 

Besides gaining a solid knowledge of state-of-the-art econometric and machine learning methods and their conditions of use, students will be trained to implement them on real data and to present the results, in oral or written form, to various audiences. Students will learn to use English in any professional context: converse in English, use technical vocabulary, understand documentation and scientific articles and write in English.

At the end of the M2, our students will have acquired the technical skills to manage and analyse massive data sets and the soft skills to communicate, which will enable them to pursue professional careers as Data Scientists or Data Analysts. The pedagogy is based on carrying out projects. The student’s capacity for analysis in a professional context, and therefore employability, is developed through an end-of-study internship, culminating in the writing and presentation of a report. Alternatively, students can select the apprenticeship track (“alternance/apprentissage”) in which they alternate between coursework at university and work in a firm.

Professional skills acquired by the end of the M2:

• Ability to manipulate, analyse and interpret data using state-of-the-art machine learning techniques and econometric methods, irrespective of their nature (e.g. quantitative, qualitative, or unstructured data such as text and images) or size.

• Competence in various programming languages (such as Python and R) and data science applications (such as dashboard visualisations), capacity to adapt quickly to any business environment.

• Ability to independently choose the most appropriate machine learning tools and implement them in order to obtain reliable and robust answers that create added value for the company, or to provide public or private administrations with analyses useful in their operations.

• Ability to communicate clearly, orally and in writing, the results of quantitative analyses to a variety of audiences such as non-specialist business managers or professional data scientists.

This M2 track is full English.

Examples of jobs held by our graduates:

• Data analyst AIRBUS HELICOPTERS
• Data Scientist STMICROELECTRONICS
• Business analyst - Health Economics IMS HEALTH
• Data scientist DIGITAL VIRGO
• Head of statistical studies POLE EMPLOI
• Data scientist KEYRUSStatistician-Economist SEABIRD
• Data scientist consultant CAPGEMINI
• Data analyst VOYAGE PRIVÉ
• Consultant-advanced analytics BUSINESS & DECISION GROUP
• Data manager INSERM
• Data scientist EQUANCY
• Consultant KPMG
• Statistical officer CER FRANCE
• Agricultural economist ARVALIS
• Business analyst ALTRAN

Watch our alumni describe their careers on Youtube. 

The syllabi will be available in a couple of weeks.

Who can apply?

Applicants must have taken two validated econometrics courses that cover at least the following: statistics (estimation, tests, confidence intervals) and the econometrics of linear and non-linear models. They must also have at least intermediate-level competence in statistical and econometric software and programming languages (such as R or Python).

Priority access is afforded to M1 students from the Master's in Econometrics-Statistics of the AMSE department at the Faculty of Economics and Management of Aix-Marseille University. However, parallel entry to M2 may be considered for students who have validated 60 credits at M1 Economics level in a programme with a strong quantitative focus.

How to apply?

Apply at the time of admissions on the dedicated platform.

This course is available in :

• Initial training
• Continuing education
• Work-study training: apprenticeships with CFA Epure Méditerranée or professional training contracts.

INTERNSHIPS AND SUPERVISED PROJECTS
For students in the classic track: at the end of the year, students perform an internship and write a master’s internship report. The objective of the report is to demonstrate the student’s ability to apply the conceptual tools acquired to questions pertaining to the professional world. The student must therefore identify the research question, implement the tools and be able to communicate the results to both a professional and an academic audience. The internship is supervised by an internship director (from the company). The report is defended in front of a jury.
For students in the apprenticeship track: there is no internship, as students alternate throughout the year between time spent at university and in their firm. Students have to write an end-of-year report and defend it in front of a jury.

LOCATION AND LANGUAGE
The Master 1 classes can be taken in Marseille or Aix-en-Provence. Courses in Marseille are taught exclusively in English. In Aix-en-Provence, most courses are taught in French and a minority in English. M2 tracks are taught exclusively in English. Both M2 tracks are thought in Marseille.

STUDIES ABROAD AND DOUBLE DEGREES
The master's program is bilingual in M1 and in English only in M2. It welcomes international students (nearly 50%), some of whom receive Eiffel scholarships. 
The student can spend the M1 abroad following one of our double degree programs with the universities of Konstanz, Tübingen, Venice, Lisbon, Kent. In this case the student spends the entire year abroad.
Erasmus exchanges are possible in the first semester of M1 if planned the previous year, or in the second semester of M1 depending on the number of places available. Students work in an international environment, speaking and writing in English.

TEACHING AND RESEARCH
The Master in Econometrics Statistics is part of the École Universitaire de recherche (EUR) AMSE, which includes almost almost a hundred researchers from Aix-Marseille Université (AMU), Centre National de la Recherche Scientifique (CNRS), École des Hautes Études en Sciences Sociales (EHESS), Centrale Méditerranée (ECM) and Sciences Po Aix. The teachers are selected according to their expertise within those institutions. The teaching staff is supplemented with practitioners.