Duration
4 semesters
Component
Faculty of Law, Economics & Management
Location(s)
Orleans
Presentation
The aim of the Master's degree in Econometrics, Statistics and ESA is to train students for careers in Data Science. This objective is based on high-level scientific training that enables students to grasp the issues involved in statistical and econometric modeling in a wide range of fields (finance-insurance, marketing, industry, etc.), guaranteeing students a high-quality integration into the workforce.
The Master's in Econometrics and Statistics has just one course: Applied Econometrics and Statistics (ESA).
Master's website: https: //www.master-esa.fr/
Skills
The training program is built around four pillars:
(1) Theoretical mastery of a wide range of statistical and econometric methods.
(2) Develop expertise in specialized software (in particular SAS software, the world leader), from data retrieval from potentially highly complex information systems(data warehouses ), to data processing (data quality issues) and statistical modeling.
(3) Provide training in economics and management, enabling students to grasp the business dimension of their statistical work and the creation of value.
(4) Develop communication skills around statistical modeling and its results, whether with Data Science specialists or non-specialists.
Useful contacts
UFR DEG International Relations Office:
https://www.univ-orleans.fr/fr/deg/international
international.deg@univ-orleans.fr
Tel: +33(0) 2 38 49 47 30
CAREER GUIDANCE AND INTEGRATION
DOIP
https://www.univ-orleans.fr/doip
02 38 41 71 72
doip@univ-orleans.fr
Organization
Knowledge control
Teaching units are assessed by continuous assessment and/or written and oral final exams. They are definitively acquired once the student has obtained an average grade, and are assigned a coefficient and European credits. Compensations are made over the semester on the basis of the overall average of grades obtained in the various teaching units, weighted by coefficients. Two assessment sessions are organized for each semester.
Special features
> Research-based teaching: the ESA Master's program is supported by a teaching team of research professors specializing in applied econometrics, all of whom belong to the Laboratoire d'Économie d'Orléans.
> Applied teaching: the other strength of the ESA Master's program in terms of students' professional integration lies in the choice made by the training team to give a very important place to learning SAS® business intelligence solutions, in all applications for both years of the Master's program. In addition, Big Data courses in R and Python are also offered.
> Recognition by the professional world: in addition to the special relationship between the ESA Master's program and SAS France, several partnership agreements have been signed in recent years. Today, our network of partners includes some twenty companies, helping us to ensure that our range of courses is well matched to market needs.
Program
Master in Econometrics and Statistics - Applied Econometrics and Statistics (ESA) course
All courses are compulsory and non-optional. The program has a Y-shaped structure. The first three semesters are common to all students. The choice between professional and research options is made at the end of semester 9. The research option is common to all Orléans Economics Masters.
Master 1
|
Teaching unit |
Coefficient/Credits |
Hours Lecture courses |
Hours Lectures Tutorial |
|
Semester 7 | |||
|
Tools for Data Science | |||
|
SAS programming |
5 |
30 |
- |
|
R programming |
2 |
24 |
- |
|
Python programming |
2 |
24 |
- |
|
Statistical tools and econometrics |
|
|
|
|
Mathematical statistics |
5 |
30 |
15 |
|
Univariate time series |
6 |
30 |
15 |
|
Analysis of qualitative data: ACM |
2 |
24 |
- |
|
Statistical learning and classification |
2 |
24 |
- |
|
Professionalization |
|
|
|
|
Insurance and actuarial techniques |
2 |
24 |
- |
|
English for Business and TOEIC |
2 |
- |
24 |
|
Professional projects |
|
|
|
|
Project 1 |
1 |
- |
- |
|
Project 2 |
1 |
- |
- |
|
Corporate partnership seminar: Data Visualization |
- |
- |
- |
|
Semester 8 | |||
|
Tools for Data Science | |||
|
New technologies in R |
2 |
12 |
- |
|
Advanced Python programming |
2 |
24 |
- |
|
Macro language in SAS |
2 |
12 |
- |
|
Statistical tools and econometrics |
|
|
|
|
Non-parametric statistics |
2 |
12 |
- |
|
Bootstrap, simulations and conformal predictions |
3 |
24 |
12 |
|
Econometrics of qualitative variables |
5 |
30 |
15 |
|
Panel data econometrics |
2 |
12 |
- |
|
Multivariate time series |
6 |
30 |
15 |
|
Professionalization |
|
|
|
|
Forecasting methods |
2 |
12 |
- |
|
Quantitative Finance |
2 |
24 |
- |
|
Professional projects |
|
|
|
|
Project 1 |
1 |
- |
- |
|
Project 2 |
1 |
- |
- |
|
Corporate partnership seminar: Data Science professions |
- |
- |
- |
|
Optional internship |
- |
- |
- |
Master 2
|
Teaching unit |
Coefficient/Credits |
Hours Lecture courses |
Hours Lectures Tutorial |
|
Semester 9 | |||
|
Statistical tools and econometrics | |||
|
Scoring methods |
4 |
24 |
- |
|
Duration models |
4 |
24 |
- |
|
Big Data analytics |
|
|
|
|
Big Data Analytics: Trees and aggregation methods |
2 |
12 |
- |
|
Big Data Analytics: Penalized regressions |
2 |
12 |
- |
|
Big Data Analytics: Vector Machine support |
2 |
12 |
- |
|
Big Data Analytics: Neural Networks |
2 |
12 |
- |
|
Big Data Analytics: Interpretable machine learning |
2 |
12 |
- |
|
Big Data Analytics : NLP with Python |
2 |
12 |
- |
|
Professionalization |
|
|
|
|
Prudential banking regulations |
2 |
12 |
- |
|
Sustainable finance |
2 |
12 |
- |
|
Financial fraud detection |
2 |
12 |
- |
|
Statistical Business Analysis |
2 |
12 |
- |
|
Oral communication |
2 |
12 |
- |
|
SAS partnership seminar |
- |
- |
- |
|
Corporate partnership seminar: Tools to combat financial fraud |
- |
- |
- |
|
Semester 10 - Career path | |||
|
Statistical tools and econometrics | |||
|
Data Mining |
2 |
24 |
- |
|
Semi- and non-parametric econometrics |
2 |
12 |
- |
|
Advanced financial econometrics |
2 |
24 |
- |
|
Professionalization |
|
|
|
|
Insurance and actuarial techniques 2 |
2 |
12 |
- |
|
Credit risk modeling |
2 |
12 |
- |
|
SAS database management |
2 |
12 |
- |
|
Implementing the SQL procedure in SAS |
2 |
12 |
- |
|
Internship |
16 |
- |
- |
|
Semester 10 - Research path | |||
|
Advanced Macroeconomics (Frontiers in Macroéconomics) |
3 |
20 |
- |
|
Advanced Econometrics (Frontiers Econometrics) |
3 |
20 |
- |
|
Advanced Microeconomics (Frontiers in Microéconomics) |
3 |
20 |
- |
|
Advanced Finance (Frontiers in Finance) |
3 |
20 |
- |
|
Advanced international and environmental economics |
3 |
20 |
- |
|
Research dissertation |
15 |
- |
- |
Admission
Admission requirements
Access to M1 :
Hold a bachelor's degree relevant to the course content.
Recommended fields of study are : Economics, Economics-Management, MIASHS, Mathematics.
Entry to M1 is selective (application and interview).
Admission to M2 :
Admission to M2 is automatic for holders of the M1 ESA d'Orléans.
How to register
Tuition fees
For students:
https://www.univ-orleans.fr/fr/univ/formation/droits-dinscriptions
For adults returning to school, professionalization contracts and VAE, consult SEFCO.
And then
Further studies
Students taking the research option can go on to study for a doctorate.
Professional integration
On completion of the course, students will be able to enter the Data Science professions, whether in the statistical research departments of banks and insurance companies, in the marketing departments of major groups, or in business services companies, etc.
Results of annual insertion surveys: https: //www.master-esa.fr/chiffres-insertion/
Occupations held :
Examples of jobs: statistical research manager, scoring research manager, marketing research manager, data mining research engineer, business intelligence engineer/consultant, model risk manager...
Sectors of activity :
On completion of the course, students will be able to take on assignments in most areas of econometrics and statistics in economics and management, and more specifically in the following sectors:
- Quantitative marketing.
- Risk management in finance and actuarial science.
- Business intelligence.
- Fraud detection