Master in Data science and engineering

 

Language of instruction : English

Master in Data science and Engineering

Responsibles

The "Big Data" phenomenon is rooted in the field of data science and engineering, which aims at developing both computer and mathematical tools for data storage,processing and analytics. An increasing volume of data is daily produced by modern day industrial processes (in fields such as energy, intelligent transport systems, health, tourism and many others...), and fuelled by the rise of multimedia content being shared and the Internet of Things in our daily life. Artificial Intelligence is now empowering applications which requires large scale and smart processing of data to build accurate predictive models.

 Key Words: Big Data, Data Science, Machine Learning, Data Mining, Deep Learning, Business Intelligence, Web Science, Artificial Intelligence

OBJECTIVES:

 

  • Combine computer and statistical sciences to develop cutting-edge and fundamental tools to efficiently address data processing problems.
  • Learn how to develop methods, algorithms and software capable to extract knowledge and insights out of huge masses of heterogonous data with several dimensions.
  • Provide a cohesive blend of technical classes in machine learning, data mining, information extraction and distributed systems coupled with fundamentals in Business, Innovation and Project Management to develop profiles which are highly valued by corporate

ADMISSION REQUIREMENTS

To be eligible, Candidates need

 

  • Bachelor's Degree (minimum 3 years of higher education) in a relevant field including undergraduate degrees in Statistics, Computer Science, Mathematics, Engineering and Physics;
  •  Strong foundations in Mathematics, Calculus (limits, derivatives, series, integrals, probability, statistics, etc.), Linear Algebra and Programming (e.g. R, Python, Java)
  • A certified B2 level in English. No requirement are needed in French as the program is fully taught in English. 

For the 18 months curriculum Master degree specifically, the candidates need:

  • A 4-years Bachelor’s Degree (minimum of 4 years of higher education) in Statistics, Computer Science, Mathematics, Engineering and Physics;
  • Demonstrable and strong foundations in Mathematics,  Database Management and Machine Learning, Calculus (limits, derivatives, series, integrals, probability, statistics, etc.), Linear Algebra and Programming (e.g. R, Python, Java)
  • Certified B2 level in English. No requirement are needed in French as the program is fully taught in English.

COURSE DESCRIPTION

Students need to validate a certain amount of credits per Teaching Unit each semester. The curriculum offers great flexibility by offering many electrive courses. Please consult the Academic Schedule and the Frequently Asked Questions for more Information about the schedule organisation.

 

SEMESTER 1 FALL (OCTOBER - JANUARY)

Language 1

(French, or another language if the student is already fluent in French)

 
Initiation Project (80h) 5

SEMESTER 2 SPRING (FEBRUARY - JUNE)

Applications (I) 10
T 3DGraph 3-D and virtual imaging (analysis and synthesis) 5
T ImSecu Imaging Security 3
T FormalMet FormalMethods-Formal specification and verification of systems 3
T MALCOM Machine Learning for Communication systems 5
T Net_Sec Network Security: practical hands on approach" 3
T APPIOT Iot Application Protocols 3
T NetSoft Network Softwerization 3
T Forensics Cyber-crime and Computer Forensics 5
T WebSem Semantic Web and Information Extraction technologies 3
T Speech Speech and audio processing 3
Fundamental in Business, Innovation and Project Management (II) 5
G SATT Sociological Approaches of Telecom Technologies 3
G ProjMan Project management 5
G Business Business Simulation 5
G TeamLead Personal Development and Team Leadership 5
Fundamentals I 10
T DeepLearning Deep Learning 3
T ASI Advanced Statistical Inference 5
T AML Algorithmic Machine Learning 3

Language 1

(French, or another language if the student is already fluent in French)

 
Semester Project 6
Supervised Semester Projects are based on real-case studies of industrial relevance. They combine a blend of theoretical and practical work (developing new prototypes and tools, testing new technologies, assessing current systems and solutions…). Students can work individually or in group of 2/3. The expected workload is 100 hours of individual work per semester. A defense is organized at the end of each semester. Projects provide students with hands-on skills by allowing them to put concepts into practice.  

SEMESTER 3 FALL (OCTOBER - JANUARY)

Applications II 10
T SysSec System and Network Security 5
T Optim Optimization Theory with Applications 3
T SSP Statistical signal processing 5
T WebInt Interaction Design and Development of Modern Web Applications 3
T QUANTIS Quantum Information Science 3
T MobServ Mobile application and services 5
T MPC Multiparty Computation and Blockchains 3
T STATS Foundations of Statistical Inference 3
T BigSec Security and privacy for Big Data and Cloud 3
T ImProc Digital Image Processing 3
T ImCod Image & Video Compression 3
Fundamental in Business, Innovation and Project Management (III) 5
G TeamLead Personal Development and Team Leadership 5
G CSE The challenges of a sustainable economy 3
G Property Intellectual property law 3
G ManagIntro Introduction to management 5
G B_INNOV How to adopt the right posture and move from idea to market! 5
G RDI Responsible Digital Innovation: Risks, Ethics and Technology 3
Fundamentals DSE II 5
T Clouds Distributed Systems and Cloud Computing 5
T DBSys Database Management System Implementation 5

Language 1

(French, or another language if the student is already fluent in French)

 
Semester Project 6
Please see the description of the Project above (Semester 2). 100h  

SEMESTER 4 SPRING (FEBRUARY - AUGUST)

RESEARCH / INDUSTRIAL INTERNSHIP 30 ECTS

The internship is to be carried out in a company or lab in France or abroad. Students work on a research/development project under the supervision of a professor and an industrial mentor.

Students are integrated as part of the staff and receive a monthly allowance, the amount of the allowance depends on the company and position. 

EURECOM provides students with an updated database of paid internship opportunities offered by companies allowing them to use this software to directly send their application to companies.