M2 Economics | Course Econometrics, big data, statistics

This is a course 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 students to contribute to the development of relevant and robust answers to questions that businesses and administrations may ask themselves in their decision-making.

Beyond a solid 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 the results, in oral or written form, to various audiences. Students will be trained in the use of English in any professional context: converse in English, use technical vocabulary, understand documentation and articles, and write in English.

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

Professional skills targeted at the end of M2 :

• Know how to manipulate, analyse, and interpret data using state-of-the-art machine learning techniques and econometric methods, irrespective of its nature (e.g. quantitative, qualitative, or unstructured data such as text and images) or size.
• Be competent in various programming languages (such as Python and R) and data science applications (such as dashboard visualisations), to be able to adapt quickly to any business environment.
• Choose independently the best relevant machine learning tools and implement them in order to obtain reliable and robust answers that contribute to the creation of value for the company or provide useful analyses to public or private administrations in the conduct of their actions.
• Communicate clearly orally and in writing the results of your quantitative analyses to various audiences such as non-specialist business managers or professional data scientists.

There are two tracks: classic track and Magistère track.

Master 2 Economics - Econometrics, Big Data, Statistics (EBDS) - Classic track (60 credits)

Semester 3 M2 Economics - Econometrics, Big Data, Statistics (EBDS) - Classic track (30 credits)

• Advanced Econometrics I: theory and applications (6 credits)
  • Non-parametric methods in econometrics
  • Automatic model selection methods
• Advanced Econometrics II: Theory and Applications (6 credits)
  • Methodology of econometric and statistical studies
  • Advanced econometrics
• Languages, software and tools for big data (6 credits)
  • Programming for big data
  • Software for big data
• Machine learning: theory and applications (6 credits)
  • Predictive methods
  • Machine learning and statistical learning
• Applications for big data: elective teaching units, choose 2 out of 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) - Classic track (30 credits)

• Non linear and multivariate models: theory and applications (9 credits)
  • Transition and duration models
  • Models for truncated and censored variables
  • Multivariate and non-linear time series
• End-of-study internship with report and defence (21 credits)

Master 2 Economics - Econometrics, Big Data, Statistics (EBDS) - Magistère track (72 credits)

Semester 3 M2 Economics - Econometrics, Big Data, Statistics (EBDS) - Magistère track (36 credits)

• Advanced Econometrics I: theory and applications (6 credits)
  • Non-parametric methods in econometrics
  • Automatic model selection methods
• Advanced Econometrics II: Theory and Applications (6 credits)
  • Methodology of econometric and statistical studies
  • Advanced econometrics
• End-of-study project (6 credits)
• Big data III (6 credits)
  • Big data tools (Hadoop, Hive, Spark)
  • Advanced machine learning
• Machine learning: theory and applications (6 credits)
  • Predictive methods
  • Machine learning and statistical learning
• Big data applications: elective teaching units, choose 2 out of 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) - Magistère track (36 credits)

• Big data IV (6 credits)
  • Big data management with SAS
  • Hands-onProject
• Non linear and multivariate models: theory and applications (9 credits)
  • Transition and duration models
  • Models for truncated and censored variables
  • Multivariate and non-linear time series
• End-of-study internship with report and defence (21 credits)

- Syllabi of courses: M2 EBDS - classic track
- Syllabi of courses: M2 EBDS - Magistère ingénieur économiste track

Who can apply?

Have two validated econometrics courses.
Have followed lessons: statistics (estimation, tests, confidence intervals) and econometrics of linear and non-linear models. Lessons in statistical and econometric software and programming languages.

The M1 of the Master's Degree in Economics of the AMSE department of the Faculty of Economics and Management of Aix-Marseille University offers a privileged access to this course. Parallel entries in M2 can however be envisaged for students who have validated 60 credits at the M1 Economics level in a course with a strong quantitative focus.

How to apply?

Apply at the time of admission on the dedicated platform.

At the end of the year, students complete an internship and write a Master's internship report. The aim of the report is to demonstrate the ability to mobilise the conceptual tools acquired to questions from the professional world. The student must therefore identify the question, implement 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 supported before a jury made up of the academic manager, the company tutor and two other persons recognized for their competence (including at least one academic).

Each course is evaluated by a written exam and/or by the realization of a file that may be presented during an oral defense. In order to limit the number of personal projects to be carried out by the student, the teachers propose transversal projects whenever possible.

This master is part of the University Research School (EUR) AMSE, which brings together nearly one hundred researchers from AMU, CNRS, EHESS and ECM. The teachers are selected on the basis of their expertise within these entities. The teaching team is also supplemented by professionals from the sector.

This training is available in :

• Initial training
• Continuing education
• open to apprenticeships, alterning university and company attendance

Professional opportunities

ROME codes :

• M1401: Conduct of Investigations
• M1403: Socio-economic studies and outlook
• M1404: Management and Investigation Management

Training specialties (NSF code) :

• 114d: Mathematics of Economics, Demographic Statistics, Mathematics of Social Sciences, Humanities
• 114g: Computer Mathematics, Financial Mathematics, Health Statistics
• 122b: Econometric Models; Methods of Economic Analysis