77 Best universities for Data Science in France

Updated: February 29, 2024

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Below is a list of best universities in France ranked based on their research performance in Data Science. A graph of 417K citations received by 16.9K academic papers made by 77 universities in France was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Claude Bernard University Lyon 1

For Data Science

Claude Bernard University Lyon 1 logo

2. Pierre and Marie Curie University

Pierre and Marie Curie University logo

3. Paul Sabatier University - Toulouse III

Paul Sabatier University - Toulouse III logo

4. University of Montpellier

University of Montpellier logo

5. Paris-Sud University

Paris-Sud University logo

6. Grenoble Alpes University

Grenoble Alpes University logo

7. University of Aix-Marseilles

University of Aix-Marseilles logo

8. University of Lorraine

University of Lorraine logo

9. University of Strasbourg

University of Strasbourg logo

10. University of Bordeaux

University of Bordeaux logo

11. University of Picardie Jules Verne

University of Picardie Jules Verne logo

12. University of Paris 1 Pantheon-Sorbonne

University of Paris 1 Pantheon-Sorbonne logo

13. University of Lille

University of Lille logo

14. National Graduate School of Engineering, Paris

National Graduate School of Engineering, Paris logo

15. Polytechnic School

Polytechnic School logo

16. Paris Dauphine University

Paris Dauphine University logo

17. University of Nice-Sophia Antipolis

University of Nice-Sophia Antipolis logo

18. Normal Superior School of Lyon

Normal Superior School of Lyon logo

19. University of Rheims Champagne-Ardenne

University of Rheims Champagne-Ardenne logo

20. TELECOM ParisTech

TELECOM ParisTech logo

21. University of Rouen Normandie

University of Rouen Normandie logo

22. Grenoble Institute of Technology

Grenoble Institute of Technology logo

23. University of Nantes

University of Nantes logo

24. National Institute for Applied Sciences, Lyon

National Institute for Applied Sciences, Lyon logo

25. Eurecom

Eurecom logo

26. Paris Descartes University

Paris Descartes University logo

27. University of Burgundy

University of Burgundy logo

28. Paris Diderot University

Paris Diderot University logo

29. National Polytechnic Institute of Toulouse

National Polytechnic Institute of Toulouse logo

30. Versailles Saint-Quentin-en-Yvelines University

Versailles Saint-Quentin-en-Yvelines University logo

31. Paris Institute of Technology for Life, Food and Environmental Sciences

Paris Institute of Technology for Life, Food and Environmental Sciences logo

32. Paris Institute of Political Studies

Paris Institute of Political Studies logo

33. School for Advanced Studies in the Social Sciences

School for Advanced Studies in the Social Sciences logo

34. University of Technology of Compiegne

University of Technology of Compiegne logo

35. Francois Rabelais University

Francois Rabelais University logo

36. University of Franche-Comte

University of Franche-Comte logo

37. SKEMA Business School

SKEMA Business School logo

38. National Institute for Applied Sciences, Rouen

National Institute for Applied Sciences, Rouen logo

39. Toulouse Business School

Toulouse Business School logo

40. ESSEC Business School Paris

ESSEC Business School Paris logo

41. University of Western Brittany

University of Western Brittany logo

42. Paris-Est Creteil Val-de-Marne University

Paris-Est Creteil Val-de-Marne University logo

43. University of La Rochelle

University of La Rochelle logo

44. Normal Superior School

Normal Superior School logo

45. University of Savoy Mont Blanc

University of Savoy Mont Blanc logo

46. University of Caen Normandy

University of Caen Normandy logo

47. University of Poitiers

University of Poitiers logo

48. National Engineering School of Mechanical and Aeronautical Engineering

National Engineering School of Mechanical and Aeronautical Engineering logo

49. National Advanced School of Engineering

National Advanced School of Engineering logo

50. University of Toulon

University of Toulon logo

51. Catholic University of Lyon

Catholic University of Lyon logo

52. University of Paris 8

University of Paris 8 logo

53. National School of Bridges and Roads

National School of Bridges and Roads logo

54. Central School of Nantes

Central School of Nantes logo

55. Montpellier SupAgro

Montpellier SupAgro logo

56. University of Technology of Troyes

University of Technology of Troyes logo

57. University of Pau and Pays de l'Adour

University of Pau and Pays de l'Adour logo

58. University of Angers

University of Angers logo

59. Paris West University Nanterre La Defense

Paris West University Nanterre La Defense logo

60. HEC School of Management

HEC School of Management logo

61. EMLYON Business School

EMLYON Business School logo

62. University of Orleans

University of Orleans logo

63. University of Clermont Auvergne

University of Clermont Auvergne logo

64. University of Evry-Val d'Essonne

University of Evry-Val d'Essonne logo

65. Polytechnic University of Hauts-de-France

Polytechnic University of Hauts-de-France logo

66. New Sorbonne University - Paris III

New Sorbonne University - Paris III logo

67. CentraleSupelec

CentraleSupelec logo

68. EHESP School of Public Health

EHESP School of Public Health logo

69. Audencia Nantes School of Management

Audencia Nantes School of Management logo

70. Grenoble Graduate School of Business

Grenoble Graduate School of Business logo

71. National Institute for Applied Sciences, Strasbourg

National Institute for Applied Sciences, Strasbourg logo

72. Kedge Business School

Kedge Business School logo

73. Jean Monnet University

Jean Monnet University logo

74. University of Southern Brittany

University of Southern Brittany logo

75. Central School of Lille

Central School of Lille logo

76. University of Avignon and the Vaucluse

University of Avignon and the Vaucluse logo

77. Paul Valery University, Montpellier 3

Paul Valery University, Montpellier 3 logo

The best cities to study Data Science in France based on the number of universities and their ranks are Villeurbanne , Paris , Toulouse , and Montpellier .

Computer Science subfields in France

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Data Science Program

The production and collection of massive amounts of data, combined with advances in storage, analysis and processing capacities, opened up unprecedented scientific perspectives in many disciplines, such as life sciences, cognitive sciences, astrophysics and quantitative sociology. The Data Science cross-disciplinary program covers the PSL education in AI and at the interfaces of other scientific disciplines. Thanks to the scientific richness of PSL in AI and other scientific disciplines and its involvement in the PRAIRIE institute , the Data Science cross-disciplinary Program offers a training of excellence in AI for students with any background.

[New in 2023] 

  • To enable all master's and PhD students at PSL University to learn about or specialize in AI and data science, whatever their field of expertise, the DATA program is launching a brand-new certifying minor from the start of the 2023 academic year.
  • The DATA program at Université PSL also offers training for academics in data science and artificial intelligence to all PSL researchers, PhD students and postdoc, adapted to their disciplines and levels (beginners, advanced, expert).

For students majoring in other subjects

If you are following a master's degree in another subject than mathematics and computer science, and if you want to include AI in your curriculum, the university-wide Data Science program includes a two-step training:

  • Two-week intensive preparation in order to learn the basics of AI and to equip you with enough background to follow several MASH and IASD Masters' courses
  • Intensive weeks on AI at the interfaces

Two preparatory weeks are offered. These are precontions for the certificate (unless the student demonstrates comparable skills as part of a previous course of study).

  •  Week 1 – Fundamentals of Mathematics and Computer Science (3 ECTS) | The basics of mathematics and computer science for data science. The week can be followed asynchronously.
  • Week 2 - Machine Learning and Database (3 ECTS) | This takes place at the end of August/beginning of September on the PariSanté Campus.

The 2024 program for these 2 weeks will be published soon.

Intensive DATA weeks are PSL weeks providing scientific and technical immersion in AI, at the interface with another discipline. 2 ECTS are awarded at the end of each week.

Details of the program can be found on the PSL weeks website .

PSL weeks from 27 November 2023 to 1st December 2024:

  • AI for Economics and Finance
  • Explainability and Interpretability in Artificial Neural Networks
  • Ethique et Intelligence Artificielle
  • NLP for Social Sciences
  • Neuro and Bio-robotics: senses and perception

PSL weeks from 4 March 2024 to 8 March 2024:

  • Digital Humanities meet Artificial Intelligence
  • Machine learning for physics and engineering
  • Green Artificial Intelligence
  • Machine learning in Genomics
  • Statistical Physics and Machine Learning
  • Data mining and modeling for behavioral sciences and beyond

COFUND: Artificial Intelligence for the Sciences (AI4theSciences)

Artificial Intelligence for the Sciences (AI4theSciences) is a doctoral program run by Université PSL. 26 Ph.D. contracts at the interfaces of artificial intelligence or big data processing are offered. AI4theSciences is supported and jointly funded by the Horizon 2020-Marie Skłodowska-Curie Actions-COFUND European program.

This unique and structured project invites an international audience. It aims to create a research community from multiple PSL laboratories and schools, along with a dozen private and public partners. AI4theSciences is one of the PSL initiatives that contribute to strengthening PSL research on a key challenge of our century. It benefits from a particularly useful and evolving environment for the doctoral students in the program and, more broadly, for the PSL research community.

All disciplines are eligible for co-funding (physics, chemistry, history, economics, etc.) as long as the doctoral dissertation includes artificial intelligence or big data processing techniques. In addition to the training courses specific to each doctoral grant, a program of training courses and specific events will be created: first courses on AI/big data, seminars, conferences, etc. Each doctoral student will benefit from dual supervision composed of a PSL dissertation adviser specialized in their discipline and a co-adviser specialized in artificial intelligence or big data techniques (possibly from an independent laboratory outside or a private partner).

Initiated in 2020, AI4theSciences is planned to run for six years.

Alexandre Allauzen (ESPCI Paris - PSL)

[email protected]

(For PSL students)

More information

Data science & AI for academics

(For PSL researchers, Phd students & postdoc)

ENSAI

France’s Top Graduate School for Statistics and Data Science

Located on the Ker Lann campus, just outside Rennes, ENSAI educates Data Scientists – for the private and public sector – capable of giving meaning to data.

We Train Data Experts

Our Data Scientists are qualified experts capable of collecting, treating, and modeling data, making it possible to derive meaning and inform decision making . ENSAI offers specializations in Risk Management, Biostatistics, Industry, Quantitative Marketing, Big Data, and Official Statistics.

Our DNA: Statistical Modeling

ENSAI graduates have advanced skills in Statistical Modeling as well as complementary skills in Computer Science and Quantitative Economics . They are unanimously recognized for the quality of the innovative scientific and operational training they receive at ENSAI and their ability to meet the needs of businesses and administrations across a wide array of sectors.

Recognized Research

ENSAI’s professors are nearly all members of the Center for Research in Economics and Statistics (CREST) research laboratory, a joint research center which includes researchers from ENSAE and the Department of Economics of Polytechnique Paris, both members of the prestigious Institut Polytechnique de Paris . The CREST lab fosters a dynamic research environment combining both fundamental and applied research in Statistics, Economics, and Computer Science which continually informs and influences ENSAI’s curriculum.

Open to the World

ENSAI has a network of academic and business partners around the globe. The school has 28 exchange partnerships in 13 European countries, as well as other exchange and double degree agreements with universities in Europe, the United States, Africa, and Asia. Additionally, 23% of ENSAI’s students come from abroad.

Not Just a Number

ENSAI graduates around 100 Data Scientists and nearly 50 Official Statisticians annually. Maintaining manageable class sizes allows the school to make sure students get the personal attention and advice they need to ensure that they succeed in their studies and begin their careers successfully.

  “Today, ENSAI educates some of the best data experts out there. Whether students go on to become Official Statisticians, Data Analysts, or Data Scientists, opportunities abound. In a field which is in constant evolution, our graduates can count on the solid foundations they have acquired at ENSAI: know-how in handling and modeling data to make sense of it. Be it in Finance, Health, Industry, Marketing, IT, Territorial Development, or Public Policy Evaluation, ENSAI graduates’ expertise will be essential in meeting the scientific, economic, and societal challenges of tomorrow.”

Ronan LE SAOUT Director of ENSAI

Part of the French Ministry of Economy and Finance, with administrative ties to France’s National Statistical Institute ( INSEE), ENSAI and its sister school ENSAE are part of the Group of National Schools of Economics and Statistics (GENES). 

ENSAE Paris - École d'ingénieurs pour l'économie, la data science, la finance et l'actuariat

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ENSAE Paris - École d'ingénieurs pour l'économie, la data science, la finance et l'actuariat

Doctoral training at ENSAE Paris is organized within two doctoral schools:

  • The multidisciplinary doctoral school of the Institut Polytechnique de Paris, of which ENSAE is one of the five member schools along with the École Polytechnique, ENSTA Paris, Télécom Paris and Télécom SudParis), co-accredited with HEC Paris.
  • The Hadamard Mathematics doctoral school, co-accredited with the Institut Polytechnique de Paris and the Université Paris-Saclay.

These doctoral schools federate a group of research teams, the first within the schools and research units of IP Paris, the second including the schools, universities and research units of the University of Paris-Saclay in mathematics. They offer future doctoral students scientific supervision at the highest international level as well as preparation for professional integration.

The doctoral school of the Institut Polytechnique de Paris

The grouping of the École polytechnique, ENSTA Paris, ENSAE Paris, Télécom Paris, Télécom SudParis within the Institut Polytechnique de Paris makes it possible to offer doctoral training of a very high level, reflecting the excellence of the founding schools that have chosen to join forces.

Internationally recognized as a degree of excellence and a marker of a very high level of expertise, the doctorate opens the door to highly qualified jobs in the public and private sectors. In addition to access to the academic world, it opens up major long-term career opportunities, based on the specific skills attained during the doctorate and widely recognized in the socio-economic world for highly qualified jobs.

The IP Paris doctoral school offers a very rich training program, reflecting its founding schools, ranging from fundamental to applied research. It is very open to the international scene, with 45% of its doctoral students coming from abroad, thus demonstrating its attractiveness on a global level.

Through its excellence and ambition, the IP Paris Doctoral School program prepares its students for outstanding scientific careers in both the academic and industrial sectors, in France and abroad.

The IP Paris doctoral school currently welcomes a thousand doctoral students, supervised by more than 800 teacher-researchers (550 of whom are qualified to direct research) in 30 research laboratories.

Two paths are open to obtain the title of doctor from the Institut Polytechnique de Paris:

  • The classic three-year doctoral path in many fields. For more information
  • The PhD Track in 5 years, for holders of a Bachelor's degree who already wish to go on to a PhD at the end of their Master's degree. For more information

Host laboratories and grants

CREST, which hosts doctoral students in economics, statistics, finance and insurance, and sociology, has some twenty research grants, usually for two years, to help students (graduates of ENSAE Paris, ENSAI, or other French or foreign higher education institutions) prepare a doctoral thesis.

  • UFR Droit Economie Management
  • UFR Médecine
  • UFR Pharmacie
  • UFR Sciences
  • UFR Sciences du Sport
  • AgroParisTech
  • CentraleSupélec
  • ENS Paris-Saclay
  • Institut d'Optique
  • Polytech Université Paris-Saclay
  • Accessibility

phd data science france

Prepare a PhD

The PhD degree attests skills acquired through research in the framework of the doctoral trainin g, which has a 3 years reference duration when the research work is carried out full-time, and a 3 to 6 years duration when the thesis is prepared part-time. The PhD degree can also be obtained by the validation of the acquired experience (VAE). 

The PhD degree - the highest internationnaly recognized by higher education - is awarded after the defense of a thesis or the presentation of a set of original scientific works. 

phd data science france

The different frameworks to prepare a PhD

  • Initial traininf (IF in French)
  • Lifelong training, excluding initial training  (FTLVin French)
  • Validation of the experience acquired (VAE  in French)

phd data science france

Admission to PhD training

  • A procedure
  • Examination of an application 

Three regimes for three PhD preparation frameworks

Registration in initial training is possible as a continuation of a master or other equivalent degree. 

  • In initial training, the PhD is full time prepared  The preparation is initially set to last  3 years. Beyond this 3 years, extensions of the duration of preparation are possible, with derogation.
  • Funding dedicated to the preparation of the thesis is requested for a registration in initial training.  The reference amount of this funding corresponds to the remuneration of the PhD contract established by public law.  Derogations from this funding threshold can be requested from the head of the establishment. 

PhD students have a main research activity in one of the research team or unit of the doctoral school. 

They also have complementary PhD activities and training, intended to develop their scientific culture, their international openness and to prepare their professional future. The PhD program is personalized and defined with each doctoral school, in a framework commin to all PhD students at Université Paris-Sacaly. 

They can have complementary activities outside research, which contribute to the preparation of their professional future (teaching mission, scientific mediation, expertise of promotion of research), limited to one sixth of their time each year.

  • A thesis monitoring committee report must be submitted by the PhD student for each of their re-registrations.

Registration in lifelong training concerns two categories of people : 

  • Those who obtained their last diploma more than a year before the desired date of first enrollment in a PhD (whether the thesis is prepared full-time or part-time, and regardless of the financing conditions envisaged)
  • Those who plan to prepare their thesis at the same time as a main activitiy other than the preparation of thesis, regardless of the date of the obtention of the last degree. The main activity means that its provides more than half of the income. 
  • Lifelong training does not two types require a funding dedicated to the thesis preparation, but its comission ensures, before the first registration, that material and financial conditions are correct.  Ultimately, candidates who have no funding to prepare their thesis and have no income from their main activity could prepare a thesis, as long as the doctoral school and the commission can ensure that the material and financial resources conditions. 

In lifelong learning, the PhD can be prepared on a part-time basis . 

  • The duration initially fixed for the preparation of the thesis depends on the time that can be devoted to its preparation . It is between 3 and 6 years. 
  • A derogation must be  requested to extend the duration of the PhD beyond the one initially fixed . Its could be discussed during the monitoring committee taht takes place each abnd every year before re-registration.

PhD students prepare their thesis in one of the research teams or units of their doctoral school . The distributio of their time between the research unit and their non-research activities is fixed from the first registration. 

As PhD student in initial training, PhD student in lifelong training also have PhD complementary activities and training, intended to develop their scientific culture, their international opennes, and to prepare their professional future. Still, their training courses are arranged to feet with the specificities of their situation. 

A thesis monitoring committee must stand each and evrey year, before re-registration. Among other things, it help to check whether the conditions of the lifelong training PhD are suitable, or deserve to be rearranged. 

Planning lifelong training arrangement procedure

Unlike initial training and lifelong training, registration for a validation of the experience acquired can only be done when the original scientific work constitutes a coherent whole, that what makes possible to consider a defense. Those works may have been carried out partly in a research unit of the doctoral school (for example, within the framework of a volunteer researcher agreement), or entirely outside the academic framework. 

  • PhD degree is awarded after a thesis defense or a presentation of the original scientific works .

To obtain a PhD degre by a validation of the experience acquired, the candidate must:

  • Write a thesis or a dissertation to assess the personal part of collective work . This dissertation or thesis will be evaluated by two rapporteurs and by a defense jury. Composition and expectations of the jury are the same as the ones for the initial training or the lifelong training PhD
  • As for the PhD students, the thesis or the dissertation will have to be legaly deposed and, if necessary published on the national portal www.theses.fr
  • An accompanying, chosen among the supervisors of the doctoral school, can be offered for the preparation of the thesis or the dissertation 
  • The work having been prepared before registration in the Validation of the experience acquired, the accompanying is not a thesis director. He does not ensure the scientific direction of the research work, but guides the candidate in the preparation of the dissertation or thesis.
  • He plays the role of thesis director for the defense (in particular to propose the defense)

Documents relating to the preparation of a validation of the experience acquired

  • Procedure  2016_05_25_procedure_de_doctorat_en_vae_0.pdf - ( 545.03 KB)
  • Admissibility file 2020_12_15_dossier_doctorat_en_vae.docx - ( 69.01 KB)
  • The vademecum "Validation of the experience acquired and PhD" vademecum_0.pdf - ( 1.76 MB)
  • The charges  tarifs-vae-complet.pdf - ( 46.2 KB)

Admission to a PhD Programme

A PhD application is a complete package that includes

  • A candidate with a research project
  • An original thesis topic
  • A thesis director
  • A team to host the research
  • A proposed funding or a proof that the material and financial conditions necessary for the successful completion of the doctoral thesis 

Candidates apply to the doctoral school that their research or team unit is attached to  via the Université Paris-Saclay application portal . Their PhD director must also be attached to that doctoral school. 

An application is considered to have been submitted only once it has been completed and finalised . To do this, candidates must : 

  • Have submitted all the documents requested by the doctoral school - A thesis subject - Information on the contions of the doctoral programme (thesis supervision, research unit) - CV - Transcripts - Other documents required by the doctoral school
  • Obtain a favourable opinion from the thesis director
  • Obtain a favourable opinion from the research or team unit director to wich the thesis directors responds

The application process is carried out via internet tool ADUM - an administrative tool for management of the PhD, from application to graduation.  Data recorded in ADUM are subjected to the RGPD regutions. 

Once the complete file has been submitted and the application has been finalised, it is examined by the doctoral school to which the applicant belongs. 

  • In case of a favorable opinion on the application file, the candidate then presents his/her doctoral project and previous research experience during an audition, in front of a admission committee organised by the doctoral scool. This hearing is required in both initial formation and lifelong training, and regardless the conditions of funding and the progress of the thesis preparation envisaged.
  • If the admissions committee gives a favourable opinion, the future PhD student may registered for the first time. 

phd data science france

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Université Paris Cité

Doctoral Studies

With its 21 doctoral schools, Université Paris Cité offers many doctoral students the opportunity to train through research in all major disciplinary fields. At the national level, once fully operational, Université Paris Cité will offfer 5% of all PhD degrees in France.

phd data science france

Université Paris Cité is committed to a doctoral policy aimed at research training and training by research. It trains future researchers and teacher-researchers as well as future high-level executives.

Astronomy and Astrophysics Ile-de-France – ED 127 Director : Mr. Thierry FOUCHET Contact : Mrs. Jacqueline PLANCY

Environmental Sciences Ile-de-France – ED 129 Director : Mrs Pascale BOURUET-AUBERTOT Contact : Mrs Laurence AMSILI-TOUCHON

Doctoral School of Computer Science, Telecommunications, Electronics of Paris (EDITE) – ED 130 Director : Mr. Carlos AGON Contact : Mrs Rose NAHAN

Language, Litterature and Imagery : civilisations and humanities – ED 131 Director : Mr. Mathieu DUPLAY Co-director : Mrs Emmanuelle ANDRE Contact : Mrs Robin CHEVALIER

Cognition, Brain, Behaviour (ED3C) – ED 158 Director : Mr Alain TREMBLEAU Deputy director UPCité   :Mrs Thérèse COLLINS Contact : Mrs Hélène JOUANNE

Cognition, Behaviour, Human behaviour (3CH) – ED 261 Director : Mrs Karine DORE-MAZARS Contact : Mrs Lucie ALEX

Legal, political sciences, economics and management – ED 262 Director  : Mrs Anémone CARTIER-BRESSON Contact : Mrs Josie YEYE

Mathematical science Paris Centre – ED 386 Director : M. Elisha FALBEL Co-director  : M. Pierre-Henri CHAUDOUARD Contact : Mrs Amina HARITI

Physical Chemistry and Analytical chemistry – ED 388 Director : Mrs Alexa COURTY Contact : Mrs Konnavadee SOOBRAYEN

Pierre Louis Doctoral School of Public Health in Paris : Epidemiology and Biomedical Information Sciences – ED 393 Director  : Mr. Pierre-Yves BOËLLE Contact : Mrs Koltoum BEN SAID

Research in Psychoanalysis – ED 450 Director : Mrs Mi-Kyung YI Co-director : Mr Thamy AYOUCH Contact : Mr Ali BRADOR

Frontiers of Innovation in Research and Education (FIRE) – ED 474 Director : Mrs Muriel MAMBRINI-DOUDET  Co-directeur David TARESTE Contact : Mrs Elodie KASLIKOWSKI

Earth and Environmental Sciences and Physics of the Universe – ED 560 Director : Mr. Fabien CASSE Contacts : Mrs Alissa MARTEAU

Hematology, Oncogenesis, and Biotherapies – ED 561 Director  : Mr. Raphaël ITZYKSON Contacts : Mr Maxime DA CUNHA / Mrs Aurélie BULTELLE

Bio Sorbonne Paris Cité – ED 562 Director : Mrs Caroline LE VAN KIM – Co-Director : Mrs Chantal DESDOUETS Contacts : Mr Louis DUVAL-KISTER

Drug Toxicology, Chemistry and Imaging (MTCI) – ED 563 Director  : Mrs Marie-Christine LALLEMAND Contact : Mrs Elisabeth HOMBRADOS

Physics in Ile de France – ED 564 Director  : Mr Frédéric CHEVY Co-director : Mr Philippe LAFARGE Contact : Mrs Monia MESTAR

Sports, Motricity and Humain mobility sciences (SSMMH) – ED 566 Director  : Mrs Isabelle SIEGLER Co-director : Mr. Bernard ANDRIEU Contact : Mrs Marie-Pierre RICHOUX

Language Sciences – ED 622 Director : Mrs Caterina DONATI Contact : Mrs Chafia AIT-HELAL

Knowledge, Science, Education – ED 623 Co-Director : Mr. Fabrice VANDEBROUCK Co-Director : Mrs Anne BARRERE Contact : Mrs Agathe TRAN

Social Sciences – ED 624

Department 1 Director : Mrs Véronique PETIT Contact : Mr. Jérôme BROCHERIOU

Department 2

Director : Mr Antoine REBERIOUX Contact : Mrs Sarah RAHMANI

More information :

Doctoral School website for more information The following content is in French French higher education system chart
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The Cifre doctorate

The Cifre ( Conventions Industrielles de Formation par la Recherche ) system allows French companies, local authorities or associations to entrust a doctoral candidate with an assignment in the framework of a research collaboration with an academic research laboratory affiliated to a doctoral school. 

Published on 16/06/2020 - Updated on 5/05/2022

If you want to obtain a doctorate that will allow you to evolve naturally in two environments with distinct requirements and to build a bridge between the academic and business worlds, the Cifre system is for you. You will be supervised by two supervisors: a thesis director, researcher or lecturer from a research unit at Sorbonne University and a scientific manager or a contact within the company. You will have a fixed-term employment contract of 3 years or even an open-ended contract (CDI).

The assignment that the company will entrust to the doctoral candidate will constitute the doctoral research project. This project will have been defined jointly by the company and the research unit and will have been validated by the doctoral school. It will fall within the framework of a collaboration agreement signed between the university and the company, an agreement which will mainly frame the sharing of intellectual property and the use of the results. 

The ANRT (National Agency for Research and Technology), which manages the CIFRE scheme on behalf of the Ministry of Higher Education, Research and Innovation, provides applicants with offers from companies and proposals for laboratory partnerships to help doctoral candidates to set up their CIFRE project.

It is also the ANRT that examines the applications. 

  • Information on the CIFRE system - ANRT website
  • Contact the Doctoral College's Corporate Relations Officer
  • Download the CIFRE presentation brochure

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Education & Training in AI

Ai education objectives.

Developing efficient and innovative AI training is twofold:

SCAI will also support the development of specific training for our teaching and research staff whose activities fall within the fields of AI applications.

Existing - and upcoming - AI training options will ensure our graduating students will be at the highest level, whether they choose to work in the private sector or to go on to careers in basic or applied research. Their education will respond to current issues in our disciplines and to public and private needs in all AI-related fields.

Bachelor, Master & PhD

Bachelor's level.

The study of AI generally requires knowledge in mathematics, engineering and computer sciences at the bachelor's level. After the first year of undergraduate studies, students can study AI methods and applications through various bachelor’s degree in computer science, mathematics or engineering. Furthermore, from 2022/23 a minor option in Data Sciences will be available to all students allowing them to develop their AI skills.

phd data science france

Bachelor's degree in computer science

Computing manifestations are numerous, from our private sphere to industrial applications: social networks, mail, games, banks, office systems, database managers, internet and telecommunications, embedded control systems, avionics, etc.

Faced with these vast societal needs, the Bachelor of Computer Science aims to provide students with abstract and technical skills as well as the knowledge of tools that will allow them to be an actor in the development of this complex and multifaceted field that is computer science.

phd data science france

Bachelor's degree in mathematics

This program provides a solid foundation of knowledge and skills in mathematics, to train students for the many professions that use mathematics

After the first year, students can enroll in this degree program providing them training for the many professions that use mathematics, either directly or after continuing their studies at Master's level or beyond. The teaching provided is based on more than 150 teacher-researchers who cover all fields of mathematics and its applications. Cross-disciplinary skills (English, computer skills, independent work) and international mobility are valued.

phd data science france

Double Bachelor's degree in mathematics and computer science

The combination of these two disciplines provides powerful tools that are more than ever needed to solve increasingly complex problems, including new issues emerging at the frontiers of science and technology.

This combination develops abstraction, analysis and technological skills, all of which are highly valued in scientific and professional environments. This program is aimed at students with a high academic potential, who are particularly motivated to follow a demanding and rigorous scientific training, which will increase their autonomy of work, their entrepreneurial spirit and their taste for contemporary science in a university setting.

phd data science france

Minor in data science

A transdisciplinary academic program under construction and available from September 2023

This option relies of Sorbonne University’s major-minor educational structure, which enables students to obtain a diploma in a major discipline along with solid grounding in a minor discipline in a related or complementary theme. The Data minor will be available to all students registered at the Faculty of Science and Engineering. Supported by SCAI and the Institute for Computing and Data Sciences (ISCD), it will bring a basis of major skills to those who are destined for scientific master’s degrees related to data and artificial intelligence.

Master's level

Sorbonne Université and its community currently offers eight master's programs in computer science, mathematics, statistics and robotics, gathering more than 300 students.

phd data science france

Master's degree in computer science

Distributed AgeNts, Robotics, Operational Research, Interaction, Decision

The Androide specialty provides both theoretical and practical education covering all areas of Artificial Intelligence, Decision, Operational Research and Interaction.

This covers issues related to "problem solving" that economic actors face, as well as questions related to the implementation of intelligent interaction processes, whether involving a human user or between autonomous entities. Its goal is to educate specialists in Information and Communication Sciences and Technologies, and mastering the concepts, models and tools of these themes.

phd data science france

Master Data Science Paris

This covers issues related to "problem solving" that economic actors face, as well as questions related to the implementation of intelligent interaction processes, whether involving a human user or between autonomous entities.

The DAC specialty provides students with fundamental knowledge of databases as well as processing, collection, manipulation & management of large amounts of data. It naturally relies on state-of-the-art techniques in data mining, artificial intelligence and computational intelligence, with a focus on machine learning (statistical and symbolic, using imperfect datasets).

phd data science france

Master's degree in computer science & mathematics (dual)

Learning & Algorithms

The Learning and Algorithms specialty (M2A) offers dual education in mathematics and computer science, focusing on data science and artificial intelligence.

It was started in 2019 and offers a comprehensive understanding of mathematical tools and methods involved in statistical learning, deep learning and artificial intelligence.

phd data science france

Master's degree in automation, robotics

Intelligent Systems Engineering

The ISI specialty prepares students for R&D-oriented careers in manufacturing or mobile robotics and on advanced automatic systems.

It covers several themes, such as the perception of the environment, the analysis of scenes, the strategy of problem solving and mechanics. Possible applications include intelligent cars, human-machine interfaces, detection systems, and the analysis and interpretation of signals (such as physiological, audio, video).

phd data science france

Master's degree in mathematics and applications

The M2 Statistics is a course in mathematical statistics, machine learning and data science at Sorbonne University, hosted by the Laboratoire de Probabilités, Statistique et Modélisation (LPSM).

The M2 Statistics aims to train tomorrow's data scientists by offering a wide range of courses from statistical learning to mathematical statistics, covering a variety of topics from the foundations of statistical theory to the practice of data science.

phd data science france

Advanced Systems and Robotics

This specialty deals with modeling and control problems of mechatronic and robotic systems, the perception of their state and their environment as well as the planning of movements and actions.

In addition to the problems of industrial robotic manipulators, teaching is also provided on issues of service robotics (Automated Guided Vehicle AGV, drone, humanoid, etc.).

phd data science france

Acoustics, Signal processing, and computer science applied to music

The program is designed to provide the scientific fundamentals and musical knowledge necessary to carry out research in the fields of musical acoustics, sound signal processing, and musical informatics. The program’s originality lies in its multi- disciplinary nature; students are required to have a high level of scientific expertise and an understanding of artistic creation.

ATIAM offers a scientific approach to the entire chain of sound and music computing from the physical and psychophysical dimensions to digital modeling and high-level symbolic structures.

phd data science france

Master's degree in computer Science

IMAGE, COMPUTER VISION, COMPUTER GRAPHICS

The Image (IMA) programme aims to provide in-depth training in the fields of image processing and analysis, computer vision and computer graphics.

The IMA course aims to provide students with an in-depth training in the fields of image processing, computer vision and computer graphics. It combines in its courses coherent arrangements ranging from the foundations of the discipline to the most advanced techniques.

phd data science france

UTC Master's degree

Complex System Engineering

The objectives assigned to the UTC-ISC Master’s degree are to provide future engineering managers with a solid scientific and technological knowledge base to be able to study (viz., to characterize and understand), model and design complex and innovative systems using a systemic multidisciplinary approach.

In this degree, two specialties are offering training related to AI: Machine learning and optimization of complex systems (AOS) Automation and robotics in intelligent systems (ARS)

phd data science france

Statistical Engineering and Data Science

This program hosted by the Statistical Institute of Paris University (ISUP) is a 2-year high-level training for careers as statisticians and data scientists in innovative sectors.

This program hosted by the Statistical Institute of Paris University (ISUP) is a 2-year high-level training for careers as statisticians and data scientists in innovative sectors. The students can follow the second year working part-time as members of the Training Center for Apprentices (CFA). In this program, academics and private sector employees teach statistics and computer science.

A transdisciplinary minor at the Master's level will soon be made available to all 3 faculties at Sorbonne University.

This minor will enable SU students in the humanities (including but not limited to digital), medicine and science to acquire the necessary computer and mathematical knowledge to understand AI.

It will teach key concepts to grasp the challenges of AI applications and developments in their respective disciplines.

The project will be set up by a multidisciplinary teaching team bringing together colleagues from the fields of humanities, medicine and science from September 2022.

Doctoral level

SCAI regroups a total number of more than 100 PhD supervisors in AI (with research habilitation or HDR). They have successfully supervised more than 380 PhDs in the past 5 years. Overall, they work in more than 20 laboratories in which they are part of teams studying all aspects of modern AI, from statistics to social sciences.

Main research units involved in AI research at SCAI

Broadly, Sorbonne University doctoral landscape is organised in 23 Doctoral Schools gathered into a Doctoral College in charge of coordinating Sorbonne University doctoral policy. http://ifd.sorbonne-universite.fr/fr/index.html

View all research units

Main Doctoral Schools involved in AI

The doctoral school is responsible for all matters related to the doctorate. It brings together a set of research units grouped around a given scientific field. The doctoral school is responsible for recruiting, monitoring, training and defending doctoral candidates. It offers courses and scientific activities to doctoral candidates and validates each doctoral candidate's individual educational plan. The main doctoral schools in AI can be found at the following link. Each doctoral school organizes a call for applications, typically open in spring of each year. Further details can be found on their respective websites.

View all doctoral schools

SCAI doctoral program in AI

The call for doctoral projects within the Sorbonne University Alliance is launched every year at the end of January. The call for doctoral applications is generally open until March and interviews take place in May. A detailed calendar is posted each year in the News section of the website .

View all PhD projects

French CIFRE Fellowships

The Cifre (Industrial agreement of training through research) system allows French companies, local authorities or associations to entrust a doctoral candidate with an assignment in the framework of a research collaboration with an academic research laboratory affiliated to a doctoral school. An important benefit is that the fellow works in the company as well as the laboratory, thereby gaining valuable experience in both worlds and understanding their different research aims and approaches.

Further information

Continuing education

Professional training is at the heart of the national AI strategy, with the goal to educate AI talent at all levels. It is therefore necessary to propose new options and new formats for continuing education in connection with changes in usage but also through concrete experiments and articulated with actions aimed at certain categories of professionals. In this frame, SCAI supports multiple efforts in continuing education, such as:

Reinforcement learning - Concepts & Practice

AlphaZero, which beats the best players in the world at Go, the OpenAI robot that manipulates a cube from all sides or solves the Rubik's cube, a group of agents that beats professional players at StartCraft or Dota2, an algorithm that reduces the cooling bill of Google's computer centres by 40%, all of which are high-profile successes of reinforcement learning that have made it a major component of artificial intelligence.

This training will give you the basics to understand reinforcement learning and will guide you towards the implementation of the most commonly used algorithms in the field.

Every Thursday from 20 May 2021 to 17 June 2021 inclusive (5 Thursdays), from 9 am to 6 pm (35 hours).

https://scai.sorbonne-universite.fr/reinforcementlearning

Professional Training in Machine Learning & Artificial Intelligence

The objective of this training is to complement and enrich the skills of professionals in the areas of data analysis and information technologies in the following fields:

Mathematical and computer tools for data processing Cloud Computing and Big Data tools Machine-learning models, including deep learning The regulatory and professional issues involving data, particularly in the areas of risk management

This training takes place 2 days/month (Friday and Saturday), from October to April.

https://formationmachinelearning.lip6.fr

Professional Training in Financial Engineering, Modeling, Simulation & Data Analytics

Sorbonne University Continuing Education and Ecole polytechnique Executive Education have joined their expertise in Science to offer the Financial Engineering Degree for executives, which is the Executive version of the famous Parisian degree in financial mathematics.

Taught by an educational team from the leading French universities, this program is focused on acquiring, completing and updating professional skills knowledge in Mathematical, statistical and numerical methods for financial markets, accounting for the recent developments of data science and artificial intelligence contributions.

The training takes place 2 days/month (Friday and Saturday), from October to May.

https://fc.sorbonne-universite.fr/nos-offres/financial-engineering-modeling-simulation-and-data-analytics/

Short training in deep learning by practice & Artificial Intelligence

This short training identifies the opportunities of deep learning to meet business needs, as well as to add value in professional projects using deep learning.

The training offers the basics and the good practices of machine learning: to understand the general principles of a neural network, to understand the types of neural architectures and to know how to select them to treat a specific problem. The course also teaches visualization and interpretation of the results provided by a neural network.

The training takes place over 3 consecutive days, usually in June.

https://scai.sorbonne-universite.fr/deeplearning

Professional Training in Machine Learning and Artificial Intelligence - Sorbonne University Abu Dhabi

Launched in 2020 and based in Sorbonne University Abu Dhabi, this high-level training in Machine Learning and Artificial Intelligence, taught in English, offers a uniquely devised blend of lectures and practical classes, specifically tailored to the needs of the twenty-first century professional.

By acquiring honed competencies in the models and the tools of machine learning and big data, trainees are able to bring about critical innovation in technological, innovation-thirsty societies. Moreover, she/he will develop a mature understanding of the opportunities and challenges posed by a large-scale use of artificial intelligence devices (such as text mining, analysis of complex networks, image processing and online advertising etc.).

The training consists of three 30-hour Teaching Modules, each spanning a full week. Modules are scheduled once a month for three consecutive months.

Short training on digital law and regulations

This training is designed for individuals without a legal background and provides you with the necessary tools to comprehend digital laws and regulations. It equips you with best practices for compliance and enables you to anticipate future developments in the field.

This course, taught by legal experts, is aimed at non-lawyers. The law and regulation of digital activities are undergoing major changes. Several major European texts have been adopted or are currently being negotiated. They cover the circulation of personal and industrial data, artificial intelligence and cybersecurity. The aim of this training course is to present this evolving legal framework, learn about best practices for complying with it and anticipate future developments.

This training takes place 2 days, 22nd and 23rd of June 2023

https://fc.sorbonne-universite.fr/pdf/fiche/?ID=32586

Short training - ChatGPT demystified

This course is aimed at non-specialists and will give you a better understanding of how ChatGPT works and its limitations.

Artificial intelligence techniques are evolving very rapidly and have a direct impact on our daily lives: search engines, recommendation systems, medical imaging and now ChatGPT. This course, taught by experts in artificial intelligence, is aimed at non-specialists. This course will give you a better understanding of how ChatGPT works and its limitations. We will decipher the underlying mechanics of artificial intelligence systems with regard to different applications (text or image generation, information retrieval, translation systems, conversational systems, etc.). Once the functional aspect has been explained, the challenge will be to take a step back from the uses of these algorithms, in particular ChatGPT, to understand the opportunities and risks associated with the introduction of these new technologies into society.

First session of this 4 hour course takes place on the 27th of June 2023 and the second session on the 22nd of September 2023.

https://fc.sorbonne-universite.fr/nos-offres/ia-chatgpt-intelligence-artificielle/

Custom training in AI

SCAI also offers tailor-made professional training at the request of companies or public and private organizations.

Do not hesitate to contact us to discuss your needs and expectations.

Future projects

The new minors, new degrees and new professional trainings are part of a strategy to structure education and training through research at Sorbonne University and with its partners.

We also plan to strengthen the offer of continuing education and training programs in AI for teaching and research staff within our institutions and organisms.

Therefore, colleagues who would like to invest in: transdisciplinary minor projects; the implementation of AI training projects for our colleagues; the construction of continuing education programs; or other related or complementary training initiatives, are welcome to contact Alexandre Guilbaud ([email protected]) for support in this process.

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Getting a PhD in Data Science: What You Need to Know

A PhD in data science prepares you for some of the most cutting-edge research in the field and can advance your career. But, whether you should pursue one depends on your own personal goals and resources. Learn more inside.

[Featured Image]:  A candidate for a PhD degree in Data Science, is sitting at her desk, working on her laptop computer.

A Doctor of Philosophy (PhD) is the highest degree that a professional can obtain in the field of data science. Focused primarily on equipping degree holders with the skills and knowledge required to conduct original research, a PhD prepares degree holders for advanced professional positions in both industry and academia. 

But, the path to obtaining a PhD is filled with many years of potentially costly study that can be discouraging to those looking for rapid career progression. Before jumping into a doctoral program, then, it’s important to define what your goals are and how a PhD may (or may not) fit into them. 

In this article, you’ll learn more about PhDs in data science, the different factors you should consider before joining one, and types of programs to consider. At the end, you’ll also find some suggested online courses to help you get started today. 

PhD in Data Science: Overview 

A Doctor of Philosophy (PhD) is the terminal degree in the field of data science, meaning it is the highest possible degree that can be obtained in the subject. Holding a PhD in data science, consequently, signals your mastery and knowledge of the field to both potential employers and fellow professionals. 

At a glance, here’s what you should know about a Data Science PhD: 

PhD vs. Master’s Degree in Data Science

There are two graduate degrees in the field of data science: a master’s in Data science and a PhD in Data Science. While both of these degrees can have a beneficial impact on your job prospects, they also have key differences that might impact which one is better for you. 

A Master’s in Data Science is a graduate degree between a bachelor’s and PhD, which usually takes between one and two years to complete. A master’s degree expands on what was learned in undergraduate school through more advanced courses in topics such as machine learning, data analytics, and statistics. Often, a master’s student in data science also pursues original research and completes a capstone project, which highlights what they learned in their program.

A PhD in Data Science is a research degree that typically takes four to five years to complete but can take longer depending on a range of personal factors. In addition to taking more advanced courses, PhD candidates devote a significant amount of time to teaching and conducting dissertation research with the intent of advancing the field. At the conclusion of their doctoral program, a PhD holder in Data Science will complete a dissertation representing a significant contribution to the field. 

Typically, bachelor’s degree holders entering a PhD program are able to earn their master’s degree as a part of their doctoral program. Those entering a master’s program, however, will usually have to apply for a PhD program even if it’s in the same department. 

Skills and curriculum 

Every PhD program is unique with its own requirements and focus. Nonetheless, they do have similar features, such as course, credit, and teaching requirements. To help you get a better understanding of how a doctoral graduate program in data science might be, here’s an example curriculum from NYU [ 1 ]: 

Complete 72 credit hours while maintaining a cumulative grade point average of 3.0 (out of 4.0) each semester.

Core courses in topics like probability, statistics, machine learning, big data, inference, and research. 

39 credit hours for elective courses in such topics as deep learning, natural language processing, and computational cognitive modeling. 

Complete teaching requirements.

Pass a comprehensive exam. 

Pass the Depth Qualifying Exam (DQE) by May 15 of their fourth semester. 

Complete all steps for approval of their PhD dissertation. 

Is a PhD in Data Science worth it? 

A PhD can open doors to new career opportunities and boost your employment prospects. But, it can also take a lot of time and money to complete. Everyone’s personal and professional goals are different, so consider these things when deciding if you should pursue a PhD in Data Science:  

Cost and time

The amount of time and money it takes to complete a PhD are perhaps the most concrete considerations one makes when deciding whether or not they should pursue a doctoral degree. According to research conducted by Education Data Initiative, the average cost of a doctorate degree is $114,300 and takes roughly four to eight years to complete [ 2 ]. 

The exact amount of time and money you might spend obtaining your doctoral degree will depend on your own circumstances and program. Before applying for a doctoral degree, make sure to review each program’s graduation requirements and costs, so you have a clear understanding of what you’re getting into. 

Data Science PhD salary 

While there are no official statistics on the salary gains data scientist earn by getting a PhD, the median salary for all data scientists is much higher than the national average in the United States. According to the U.S. Bureau of Labor Statistics (BLS), for example, the median salary for data scientists was $100,910 as of May 2021 [ 3 ]. 

Typically, the entry-level degree to get a data science position is a bachelor’s degree, meaning that even just an undergraduate degree could help you land a job that earns a higher than average salary. Nonetheless, a PhD will likely prepare you for more advanced positions that could offer higher pay than less specialized roles. 

Data Science PhD programs 

There are several types of doctoral programs that you might consider if you would like to obtain a PhD in data science. These include: 

PhD in data science online

An online PhD program may appeal to individuals who are interested in a more flexible program that allows them to complete their coursework at their own pace. Often, online programs can also be cheaper than their in-person counterparts, though they often offer less opportunities for networking and mentorship. If you’re an independent, self-starter looking for a program that can fit into their already busy life, then you might consider an online PhD program. 

PhD in data science in-person

An in-person PhD program is a more traditional, educational method in which you attend classes on campus with your peers and instructors. In addition to providing doctoral-level instruction, you will also have more opportunities to network and gain more personalized instruction than you will likely encounter through online programs. In-person programs tend to be more expensive and inflexible than in-person ones.

If you prefer real-world instruction, networking opportunities, and a more rigid structure, then you might consider an in-person doctoral program. 

Alternatives 

As an alternative to a PhD program, you might also consider obtaining a master’s degree. While covering some of the same material as a doctoral program, a master’s usually takes much less time and money to complete.

If you’re motivated primarily by the desire to boost your chances of landing a job and gaining financial stability, then a master’s degree program might better help you achieve your goals.

Learn more about data science 

Whatever your educational goals, data science requires extensive knowledge and training to enter the profession. To prepare for your next career move, then, you might consider taking a flexible online course through Coursera. 

The University of Colorado Boulder’s Data Science Foundations: Data Structures and Algorithms Specialization teaches course takers how to design algorithms, create applications, and organize, store, and process data efficiently. Their online Master of Science in Data Science , meanwhile, teaches broadly applicable foundational skills alongside specialized competencies tailored to specific career paths in just two years of instruction. 

Article sources

NYU Center for Data Science. “ PhD in Data Science, Curriculum , https://cds.nyu.edu/phd-curriculum-info/.” Accessed September 27, 2022. 

Education Data Initiative. “ Average Cost of a Doctorate Degree ,  https://educationdata.org/average-cost-of-a-doctorate-degree.” Accessed September 27, 2022. 

US BLS. “ Occupational Outlook Handbook: Data Scientists , https://www.bls.gov/ooh/math/data-scientists.htm#tab-1.” Accessed September 27, 2022. 

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Coursera staff.

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Institut Polytechnique de Paris

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Institut Polytechnique de Paris

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ENSTA

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ENSAE

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Télécom SudParis

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  • Applied Mathematics and Statistics Program

Master Year 2 Data Science

Master Year 2 Data Science

WHY ENROLL IN THIS PROGRAM?

Asset n° 1 .

Master key tools and skills for data scientists based on an interdisciplinary approach

Lay the foundations of your future career by pursuing a PhD track in Data Science or following an apprenticeship program

Open up numerous job opportunities as data scientists, data analysts, or in academia

  • Description

Today, major players in the world of business are increasingly aware of the potential of their data and are looking for ways to extract as much useful information as possible. Data scientists are in charge of retrieving, storing, organizing, processing this mass of information to create value. This is a hybrid profile requiring a solid background in mathematics and statistics, mastery of data management and processing tools and infrastructure, as well as curiosity and a thirst to understand.

The objective of the Master in Datascience is to train experts in this field. At the end of the training, the students have acquired skills in mathematics of statistical learning, in deep learning, reinforcement learning, optimization, and big data infrastructures among others. In particular, these skills are developed through practical projects and data science competitions.  At the end of the year, both the results at the chosen courses and the professional project are evaluated to validate the Master. 

This program allows students to:

  • become experts on statistical learning and artificial intelligence
  • gain a comprehensive training in the various disciplines constituting data science, with a strong emphasis on the mathematics and statistics methodology
  • master sophisticated techniques both theoretically and in practice

Data Science has a strong impact on many sectors. There is currently a large worldwide shortage of data scientists and data analysts. Students from Data Science and Big Data courses are therefore eagerly awaited on the global job market. Like all fields of breakthrough innovation (e.g. biotechnology and e-medicine), there is a high need for high-level engineers and doctoral candidates.

On average, almost 25% of the students pursue with a PhD, while the others pursue in the industry.

42 ECTS of courses to validate over the 3 quarters

18 ECTS for internship

Students of the Master can choose various courses, from very theoretical and mathematically involved courses, to more practical ones. In broad terms, the courses cover*:

  • Convex optimization 
  • Reinforcement learning 
  • Graphical Models 
  • Deep Learning
  • Theory of Deep Learning 
  • Machine Learning Theory 
  • Machine Learning for Audio, Text, Graph, Dynamical Data
  • Markov Chain Monte Carlo methods 

The courses also cover**:

  • Large scale machine learning/ Big Data 
  • (Advanced) Optimization
  • Machine Learning & advanced methods
  • Business or ethical aspects of ML 

*quite similarly to the MVA Master

** more specific to the Master Data Science - examples in (3) that are not covered by the MVA Master : Time Series, Causal Inference, Tail Event Analysis...

Large Scale Machine Learning/Big Data

Optimization

Machine Learning 

Machine Learning

Business and ethical aspects of ML

Large Scale Machine Learning

Business and Ethical Aspects of ML 

  • Internship of minimum 16 weeks
  • From April to end of August

Admission requirements

Academic prerequisites.

Completion of the first year of a Master in mathematics at Institut Polytechnique de Paris or equivalent in France or abroad.

Language prerequisites

How to apply.

Applications can be submitted exclusively online. You will need to provide the following documents:

  • Two academic references (added online directly by your referees)
  • Statement of purpose

You will receive an answer in your candidate space within 2 months of the closing date for the application session.

Fees and scholarships

Fees for 2023-2024 are :

  • EU/EEA/Switzerland students: 4648€
  • Non-EU/EEA/Switzerland students: 6839€
  • Engineer students enrolled in one of the five member schools of Institut Polytechnique de Paris (Ecole polytechnique, ENSTA Paris, ENSAE Paris, Télécom Paris and Télécom SudParis): 159€
  • Special cases: please refer to the "Cost of studies" section of the FAQs

Find out more about scholarships

Please note that fees and scholarships may change for the following year.

Applications and admission dates

Coordinators.

Rémi Flamary

Program Office 

Stéphanie Clevenot

Contact the Master Program Office

General enquiries

[email protected]

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Imagine working side-by-side with industry leaders in your field of study. Imagine building upon the skills you've learned in the classroom within a family of legendary brands passionate about creating world-class experiences on a global scale. Now, imagine yourself in a paid Disney Internship.

Audience Modeling and Data Science Graduate Intern, Summer 2024

Job summary:.

About the Role and Program

Supporting the Disney Advertising Sales organization within our Disney Entertainment Television segment, the Data & Measurement Science team are the domain authorities in each of the following areas:

Measurement

Addressability

Programmatic

Data-forward business development

We are an outstanding organization made up of Data Scientists, Data Strategists, Analysts and business domain experts. We collaborate with both internal and external teams to understand how we can lead data-driven initiatives in support of the Advertising Sales organization.

What You Will Do

Does this sound like the team for you? We are looking for an intellectually curious individual to join us as an Audience Modeling and Data Science Graduate Intern in The Walt Disney Company’s DET segment, specifically within our Data Strategy organization. In this role, you will be a sophisticated Data Science Intern who will be responsible for the following:

Partnering with senior members of the Data Strategy team to develop knowledge of advertising sales as an industry and resulting datasets that DET applies to develop predictive models and analytical products.

Lead a project end-to-end, in close collaboration with a mentor, and understand how the team’s work contributes to the Ad Sales organization overall.

Attend team workshops, prioritization and update meetings to understand ways of working and actively participate and contribute to cross-functional initiatives.

Perform ad-hoc analysis to support various projects across our Data & Measurement Science team.

Assist in detailing data models, processes, and other initiatives as specified by leadership and peers.

Collaborate with full-time analysts to gain perspective on different aspects of day-to-day projects and responsibilities.

Network with individuals across the Data & Measurement Science organization, as well as the broader Sales teams, to explore areas of opportunity for Data Science in Advertising.

Required Qualifications & Skills

Course or project-based experience using analytical software to manipulate data (SQL and Python preferred).

Course or project-based work using basic data Engineering Concepts (ETL, ELT).

Shown ability to communicate technical concepts to a technical audience.

Highly engaged, ambitious problem solver who is self-motivated and eager to learn.

Eager to collaborate, become part of a team, and lean-in on asking questions from leadership.

  • Currently in the process of obtaining a Master’s in Computer Science, Applied Mathematics, Statistics, or other related quantitative field.

Eligibility Requirements & Program Information

Candidates for this opportunity MUST meet all the below requirements:

At the time of application, be enrolled in an accredited college/university taking at least one class in the semester/quarter (spring/fall) prior to participation in the internship program AND returning to school the semester/quarter following the internship OR currently participating in a Disney College Program or Disney Internship

Be at least 18 years of age

Possess unrestricted work authorization

Have not completed one year of continual employment on a Disney internship or Disney College Program

Additional Information

The approximate dates of this internship are May/June through July/Aug 2024

Able to provide own housing for the duration of the internship program in Santa Monica, CA

Must provide/have reliable transportation to/from work

About Disney Entertainment:

At Disney Corporate you can see how the businesses behind the Company’s powerful brands come together to create the most innovative, far-reaching and admired entertainment company in the world. As a member of a corporate team, you’ll work with world-class leaders driving the strategies that keep The Walt Disney Company at the leading edge of entertainment. See and be seen by other innovative thinkers as you enable the greatest storytellers in the world to create memories for millions of families around the globe.

About The Walt Disney Company:

The Walt Disney Company, together with its subsidiaries and affiliates, is a leading diversified international family entertainment and media enterprise with the following business segments: Disney Entertainment, ESPN, Disney Parks, and Experiences and Products. From humble beginnings as a cartoon studio in the 1920s to its preeminent name in the entertainment industry today, Disney proudly continues its legacy of creating world-class stories and experiences for every member of the family. Disney’s stories, characters and experiences reach consumers and guests from every corner of the globe. With operations in more than 40 countries, our employees and cast members work together to create entertainment experiences that are both universally and locally cherished.

This position is with Disney Advertising Sales, LLC , which is part of a business we call Disney Entertainment .

Disney Advertising Sales, LLC is an equal opportunity employer. Applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, sexual orientation, gender identity, disability, protected veteran status or any other basis prohibited by federal, state or local law. Disney fosters a business culture where ideas and decisions from all people help us grow, innovate, create the best stories and be relevant in a rapidly changing world.

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Undergraduate wins UC Graduate Deans’ Leadership and Research Award

E rik Hakopian , a fourth-year undergraduate student and neuroscience major at UCR, has been awarded a UC Graduate Deans’ Leadership and Research Award, a prestigious UC-wide honor. The award, accompanied by a cash prize of $500, is given to scholars who showed extraordinary leadership during their tenure as University of California Leadership Excellence through Advanced Degrees, or UC LEADS , scholars. 

The UC LEADS program prepares promising students for advanced education in science, technology, mathematics, and engineering. Hakopian joined the two-year fellowship program in April 2022; his tenure will end when he graduates this May. Each year, only 6-8 people are awarded UC LEADS fellowships per UC campus.

Erik Hakopian

Hakopian received the award on March 2 at the Koret UC LEADS Research and Leadership Symposium held at UC Berkeley. The annual symposium serves as an opportunity for mentors and scholars from all UC campuses to meet as an intellectual community. Scholars share their research through poster presentations, attend professional development workshops and panels, and listen to keynote addresses from speakers in government and industry.

Hakopian’s poster presentation was titled “Exploring the effects of MK-801 on functional connectivity of the septo-hippocampal networks via machine learning algorithm classification.” MK-801 is an antagonist — a chemical substance that binds to and blocks the activation of certain receptors on cells — that induces schizophrenia-like symptoms in rodents. Hakopian explained that brain disorders can offer insight into how the brain works. 

“When something doesn’t work properly in the brain, we can compare its outcome to when it is normally functioning,” he said. “Schizophrenia is one of the brain disorders resulting in delusions and hallucinations. Thus, studying it can reveal many functionalities of the brain. One of the functionalities I’m analyzing is a specific network of six regions in the brain called the septo-hippocampal network that is responsible for memory and learning.”

Drawn to neuroscience at an early age, Hakopian believes the biggest breakthrough in the field will be when the brain is fully understood. 

“All the advancements in AI models, self-driving cars, and much more started from the attempt to replicate the architecture of the human brain,” he said. “I would like to understand how we interact with information, store it, and use it to innovate.”

Born in Iran, Hakopian’s family struggled to make ends meet. His parents took on debts to enroll him in school so he could learn to read and write.

“My journey of becoming a leader started in Iran, where the simplest human rights were nowhere to be found,” he said. “I was 14 when I first participated in a big protest for women’s rights. The threat of tear gas, batons, and even rubber bullets being used against the people loomed large, casting a shadow of fear over every march and rally. The potential consequences of speaking out were severe. Capture could mean imprisonment and harsh punishments, leaving many with a difficult choice between silence and risking their safety.”

Hakopian was 18 years old when his family arrived in the United States. In high school, he struggled to understand what people said and relied on Google to learn English. At Pasadena City College, he served as treasurer for the Armenian Student Association club. After transferring to UCR, he was accepted into the UC LEADS program. It marked, he said, the most transformative occasion of his career.

During his first UC LEADS summer internship, Hakopian met with many students who faced challenges getting into labs and were confused about research. It inspired him to create a discord server for students wanting to do research. More than 300 students joined in the first two weeks. He also helped establish the Academic, Preparation, Recruitment, and Opportunities - Student Ambassador Club on campus, taking on the role of vice president. 

“Erik is an intellectually curious student with strong writing and analytical skills,” said Anthony Macías , a professor of ethnic studies who teaches ‘Introduction to the Study of Race and Ethnicity,’ a course Hakopian took. “He has an open mind and rigorously considers every concept from different angles. His critical thinking and reading comprehension ensure that he arrives at logical, well-reasoned conclusions. I have seen him respectfully interact with his classmates, leading to generous, fruitful collaborations. Students like Erik are a breath of fresh air, and they keep my fire for teaching burning brightly.”

Vasileios Christopoulos , an assistant professor of bioengineering who has mentored Hakopian for nearly two years, also had praise for him.

“Erik is an exceptional and passionate student,” he said. “He comes with new ideas in research and can spend hours and days in the lab working on current projects. He has developed a pipeline for performing functional connectivity analysis in the animal brain. He also designed and developed an implantable system for mounting the ultrasound probe into the head of the animals so that we can perform experiments in awake and behaving animals.”

After he graduates with a bachelor’s in neuroscience, Hakopian plans to apply to graduate school for his doctoral degree, with a focus on how information is stored and recalled in the brain.

“I know I still have a lot to do to achieve my dreams, but this award is a reminder of how far I have come as well as my family’s and my own hard work,” he said. “I’m excited about the future and to work even harder.”

Hakopian’s other honors include an Honors Excellence in Research (HEIR) Scholarship, Hays Capstone Project Fellowship, winner of the Blackstone LaunchPad Ideas Competition, and a Maria Franco-Gallardo Scholarship.

Related Awards

Marketing data manager named a “rising star”, psychologist elected fellow of association for psychological science, ucr athletics director among the 100 most impactful, gift to ucr results in new undergraduate fellowship.

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    Audience Modeling and Data Science Graduate Intern, Summer 2024 Apply Now Apply Later Job ID 10075368 Location Santa Monica, California, United States / Seattle, Washington, United States Business Disney Entertainment Date posted Mar. 12, 2024

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