Data Science, Machine Learning & Knowledge (DAC)

All industrial and economic sectors are confronted with an explosion of data generated by their activities and collected by increasingly powerful infrastructures (sensors, networks, databases). The professionals concerned are progressively discovering the potential of this data to enrich and optimize their activity owing to new computing technologies to store, manage and analyze large quantities of data, and advanced tools to extract and visualize useful, synthetic information.

The development of these new tools requires data scientists with fundamental knowledge in computer science and statistics, and a strong command of the associated computer technologies.

Data Science, Machine Learning & Knowledge (DAC)


The pedagogical objective of the DAC course is to provide fundamental knowledge in all areas of artificial intelligence focused on the use of data, the production of knowledge and the implementation of intelligence services:

  • databases for collecting, storing, managing and querying large amounts of complex data,
  • Information search and data mining for filtering, analysis and extraction of information,
  • Machine learning to extract statistical and symbolic models from imperfect data,
  • Computational intelligence to reason and utilize knowledge extracted from data.



Les métiers visés par le parcours DAC sont des emplois de concepteurs, de développeurs et d'utilisateurs d'outils intelligents dans tous les domaines importants nécessitant des compétences fortes en traitement, analyse, enrichissement des données. L’expertise nécessaire pour ces métiers évoluent rapidement et nécessite le suivi continu des activités et résultats scientifiques concernés. Le parcours DAC vise également des emplois dans la recherche scientifique et la R&D. 

On peut citer les domaines suivants :

  • Gestion du Web, Web advertising, Conception de plateformes sociales
  • Business Intelligence, Customer Relationship Management (CRM)
  • Recherche d'informations et moteurs de recherche sur le web et dans des plateformes sociales
  • Database tuning (administrateur de BD), Data analyst, Data architect, Data Engineer, Data manager on distributed architectures (cloud, data grid, data center), Scientific data manager, technology watch, Web architect

Les entreprises typiquement intéressées par le profil des étudiantes et étudiants de ce parcours sont des acteurs du traitement de l'information, industriels (OpenData, Etalab, Internet memory, Google, Yahoo !) ou académiques (BNF, INA), des grandes entreprises exploitant des solutions complexes telles que SAP (Accenture, Total), de la recherche d'information et de la fouille de données (Exalead, BlogSpirit, KXEN), ainsi que des grands groupes dans des domaines divers tels que la finance...

Du point de vue académique, le parcours ouvre naturellement vers des postes d'enseignante-chercheuse ou enseignant-chercheur et de chercheuse ou chercheur. Le but est de conserver un taux de poursuite en thèse de l'ordre de 50 %.


Organization of the training

The first semester of the M1 has a common core offering an important mutualization of courses with other programs of the computer science field. Two compulsory courses introduce the main models and languages for storing and accessing structured and semantic data, and for knowledge representation. These two courses are complemented by courses from other tracks that aim to provide a set of mathematical and computational tools necessary for the training as well as some other opportunities. Subject to schedule compatibility, we will also allow students, if they wish, to take a so-called "free" UE (belonging to another course of the Computer Science Master, or even another Master), which does not appear in the following table, and which would correspond to a coherent professional project.

From the second semester of the M1program, we propose three different competencies, each one offering a set of specific skills to the students, via a personalized arrangement of UE. These three competencies are :

  • The "Learning" competency (APP) offers UEs in the areas of information retrieval and data mining applied to complex data analysis, scientific and industrial intelligence and social media.
  • The "Databases" (DB) competency offers UEs in the field of databases applied to the management of complex and distributed large-scale data.
  • The "Artificial Intelligence" (AI) competency offers courses in knowledge modeling and symbolic learning applied to complex and uncertain information (documents, semantic web).

The courses in each profile are complementary to the courses in the other two profiles and enable students to personalize their training.

The first semester of the M2 program offers specialized courses, each of which provides a set of specific skills for the different competencies. The students’ orientation towards a profession will be established through their internship in the second semester of M2, which takes place during the entire second semester and can be carried out in a research laboratory or company, with the emphasis on either research or development.

The students registered in Master M2 DAC with the sufficient prerequisites can follow UE related to advanced mathematical aspects (convex optimization, non-convex, statistical and automatic learning). Please see the detailed content on  


First semester (M1-S1)

You must choose 5 UEs from the following UEs and follow the UE d'Insertion Professionelle (IP).

Acronym Title ECTS Course  
MLBDA Advanced Database Models and Languages 6 DAC/ANDROIDE Mandatory
LRC Logic and Knowledge Representations 6 ANDROIDE/DAC Mandatory
MAPSI Probabilistic and Statistical Models and Algorithms for Computer Science 6 DAC/IMA Highly recommended
IL Software Engineering 6 STL Recommended
COMPLEX Complexity, Probabilistic and Approximate Algorithms 6 SFPN Recommended
MODEL Numerical and symbolic modeling 6 BIM Recommended
MOGPL Modeling, Optimization, Graphs, and Linear Programming 6 ANDROIDE Recommended
BIMA Basics of Image Processing 6 IMA Recommended

Second semester (M1-S2)

The second semester consists of:

  • 2 compulsory UEs: 
Acronym Title ECTS
PLDAC Software project, bibliographic research and business conferences 3
English English



  • and 4 UEs to be chosen among those below
Acronyme Intitulé ECTS
IAMSI Artificial Intelligence and Symbolic Information Handling 6
ML Machine Learning 6
RITAL Information Retrieval and Natural Language Processing 6
SAM Storage and Access to Megadata (Scalable Datastores) 6
DJ Decision and games 6

Semester 3

During this semester, there are 5 UEs to choose from listed on the table below, 4 of which are among the first 6 (DAC course).

Acronym Title ECTS Course
AMAL Advanced Machine Learning and Deep Learning 6 DAC
BDLE Large Scale Databases 6 DAC
XAI eXplainable Artificial Intelligence 6 DAC
REDS Research in Data Science and Methodology 6 DAC
LODAS Linked Open Data, Symbolic Learning 6 DAC
AMAL [dependence on AMAL] Reinforcement Learning and advanced Deep Learning 6 DAC
RDFIA Pattern recognition for image analysis and interpretation 6 IMA

Semester 4

Acronym Title ECTS Course
Stage End of Master 2 internship 27 DAC
IP Professional Placement 3 DAC


Upon graduation, the graduate will have mastered:

  • the issues, problems and context of large-scale information processing
  • the basic tools of artificial intelligence
  • symbolic and numerical technologies for data-driven machine learning
  • basic tools for information retrieval
  • the different components of an operational data mining tool
  • the functioning of search engines, text, image, speech and video.

They will also be able to implement and innovate the design of :

  • large-scale data management, collection and analysis systems,
  • data mining, information retrieval and technology watch tools,
  • machine learning and pattern recognition algorithms.


Target audience and prerequisites

The recruitment in M1 is mainly done at the L3 level (or equivalent) in Computer Science or Computer Science/Mathematics. Well-motivated candidates from other scientific fields may also be considered.

At the M2 level, the program can accommodate a few external candidates with compatible prerequisites, and in particular students in their final year of engineering schools (Centrale, CNAM, ENSIIE, EPITA, ParisTech, etc.) who want to complete a double degree.

Candidates must have solid knowledge of computer science (algorithms and programming, databases, logic...). Fundamental notions in mathematics (probability, statistics) are strongly advised.



Géraldine BOMPARD