M2 Econometrics Statistics | Econometrics and Data Science (EDS)

This is a Data Science course built on solid statistical and econometric foundations. Students will learn to code and applymachine learning techniques, and to interpret and communicate the results of their scientific projects. In this way, students will be able to contribute to the development of relevant and robust answers to the questions that companies and administrations may ask themselves in their decision-making processes.

In addition to a sound knowledge of econometric and machine learning methods and their conditions of use, students will be trained to implement them on real data and to present results, orally or in writing, to a variety of audiences. Students will be trained to use English in any professional context: conversing in English, using technical vocabulary, understanding documentation and articles, writing in English.

By the end of M2, our students will have acquired the technical skills to manage and analyze massive data sets, and the soft skills to communicate, and thus be in a position to pursue professional careers as Data Scientists or Data Analysts. Teaching is based on project work. The student's ability to analyze data in a professional context, and thus his or her employability, is developed by an end-of-study internship, completed by the writing and presentation of a report. The course is open to "sandwich/apprenticeship" students, who alternate between attending university and working in a company.

Professional skills targeted at the end of M2:

• Know how to manipulate, analyze and interpret data usingmachine learning techniques and state-of-the-art econometric methods, whatever its nature (e.g. quantitative, qualitative or unstructured data such as text and images) or size.
• Be proficient in various programming languages (such as Python and R) and data science applications (such as dashboard visualizations), so as to be able to adapt quickly to any professional environment.
• Autonomously select the best relevant machine learning tools and implement them to obtain reliable and robust answers that contribute to the creation of value for the company or provide useful analyses for public or private administrations in the conduct of their actions.
• Clearly communicate the results of your quantitative analyses, both orally and in writing, to a wide range of audiences, from non-specialist business managers to professional data scientists.

Career opportunities

ROME codes :

• M1401 : Survey management
• M1403 : Socio-economic studies and forecasts
• M1404 : Survey management and administration

Training specialties (NSF code) :

• 114d: Mathematics of economics, demographic statistics, mathematics of social sciences and humanities
• 114g: Computer mathematics, financial mathematics, health statistics
• 122b: Econometric models; Methods of economic analysis

The courses are divided into two programs: classical training and the Magistère option.

Master 2 Economics Econometrics, Big Data, Statistics (EBDS) - Standard course (60 credits)

• Semester 3 M2 Economics Course in Econometrics, Big Data, Statistics (EBDS) - Standard course (30 credits)
• Advanced Econometrics I: theory and applications (6 credits)
  • Non-parametric methods in econometrics
  • Information reduction methods
• Advanced econometrics II: theory and applications (6 credits)
  • Methodology of econometric and statistical studies
  • Advanced econometrics
• Big data languages, software and tools (6 credits)
  • Programming for big data
  • Software for big data
• Machine learning: theory and applications (6 credits)
  • Forecasting methods
  • Machine learning and statistical learning
• Big data applications: optional teaching units, 2 units to be chosen from 4 (6 credits)
• Big data and quantitative marketing (3 credits)
  • Big data and quantitative marketing
• Big data and finance (3 credits)
  • Big data and finance
• Big data: other applications (3 credits)
  • Big data: other applications
• Big data and economics (3 credits)
  • Big data and economics

• Semester 4 M2 Economics Econometrics, Big Data, Statistics (EBDS) - Traditional training (30 credits)
• Non-linear and multivariate models: theory and applications (9 credits)
  • Models of transitions and durations
  • Models for truncated and censored variables
  • Multivariate and nonlinear time series
• Internship with report and defense (21 credits)

Master 2 Economics Econometrics, Big Data, Statistics (EBDS) - Magisterium Option (72 credits)

• Semester 3 M2 Economics Course in Econometrics, Big Data, Statistics (EBDS) - Magister Option (36 credits)
• Advanced Econometrics I: theory and applications (6 credits)
  • Non-parametric methods in econometrics
  • Information reduction methods
• Advanced econometrics II: theory and applications (6 credits)
  • Methodology of econometric and statistical studies
  • Advanced econometrics
• Final project (6 credits)
• Big data III (6 credits)
  • Big data tools (Hadoop, Hive, Spark)
  • Advanced machine learning
• Machine learning: theory and applications (6 credits)
  • Forecasting methods
  • Machine learning and statistical learning
• Big data applications: optional teaching units, 2 units to be chosen from 4 (6 credits)
• Big data and quantitative marketing (3 credits)
  • Big data and quantitative marketing
• Big data and finance (3 credits)
  • Big data and finance
• Big data: other applications (3 credits)
  • Big data: other applications
• Big data and economics (3 credits)
  • Big data and economics

• Semester 4 M2 Economics Econometrics, Big Data, Statistics (EBDS) - Magister Option (36 credits)
• Big data IV (6 credits)
  • Big data management with SAS
  • Project
• Nonlinear and multivariate models: theory and applications (9 credits)
  • Models of transitions and durations
  • Models for truncated and censored variables
  • Multivariate and nonlinear time series
• Internship with report and defense (21 credits)

- Access course syllabi for M2 EBDS - conventional training

- Click here for syllabi of M2 EBDS courses - Magistère economist engineer option

Who can apply?

Two validated econometrics courses.

Have taken courses in: statistics (estimation, tests, confidence intervals) and econometrics of linear and non-linear models. Statistical and econometric software and programming languages.

The M1 Master's degree in Economics from the AMSE department of Aix-Marseille University's Faculty of Economics and Management offers privileged access to this course. However, parallel entry into M2 may be possible for students who have completed 60 M1 Economics credits in a course with a strong quantitative emphasis.

How to apply?

Apply at the time of admissions on the dedicated platform.

At the end of the year, students carry out an internship and write a Master's internship report. The aim of the report is to demonstrate the student's ability to mobilize the conceptual tools he or she has acquired to address issues arising in the professional world. The student must therefore identify the question, apply the tools, and know how to communicate the results to both a professional and academic audience. Supervision is provided by an academic and an internship supervisor (a member of the company). The report is defended before a jury comprising the academic supervisor, the internship supervisor and two other persons recognized for their competence (including at least one academic).

Each course is assessed by means of a written exam and/or the production of a portfolio, which may be presented at an oral presentation. To limit the number of personal projects to be carried out by students, teachers propose cross-disciplinary projects whenever possible.

This Master's program is part of the AMSE Ecole Universitaire de Recherche (EUR), which brings together nearly one hundred researchers from AMU, CNRS, EHESS and ECM. Teachers are selected on the basis of their expertise within these entities. The teaching team is complemented by industry professionals.

This course is available in :

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