Program Objectives of the Master's Degree in Digital Economics
This academic track provides expert training in digital economics and methods for analyzing mass data. Harnessing this type of data requires new skills to be able to process high volumes of input and extract useful information. This track therefore aims to train quantitative economists in processing and modeling large, complex datasets to shed light on the decisions of businesses and institutional stakeholders. Employment opportunities are highly varied: data analyst, consultant, economic expert, etc.
Skills acquired :
- Make use of statistical and econometric tools to obtain reliable and robust answers, to shed light on businesses and public or semi-public institutions’ options
- Learn about quantitative methods for processing massive databases
- Get trained in computer programming to process large and complex databases
- Report on the results of economic, statistical and/or econometric results to different audiences, orally and in writing
When you enroll in a Master's program, you also join Université PSL. Ranked in the top 50 universities in the world (THE and QS), PSL offers excellent graduate programs at the Master's and PhD level, which benefit from the scientific capabilities of its member institutions. The degree is completed at the Université Paris Dauphine-PSL and awarded by Université PSL.
- Types of education
- Initial training
- Executive Education
- Language(s)
- English
- ECTS Credits
- 60 credits
- Internship
- 12 weeks
- Capacity
- 30
- Type of Diploma
- Diploma from a major institution conferring the Master's degree
- Academic Year
- 2023/2024
After Dauphine
found before graduation
Professional integration rate
Median salary
Contacts
RÉNÉ AID
University Professor, Université Paris Dauphine - PSL, Co-Director of the 2nd year of the Master's Degree in Digital Economics
Madalina Olteanu
University Professor, Université Paris Dauphine - PSL, Co-Director of the 2nd year of the Master's Degree in Digital Economics
Lucie NEUVILLE
Teaching assistant