Master in Data Science and Engineering

General

Read more about this program on the school's website

Program Description

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, etc.), 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 require 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 that are highly valued by corporate.

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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 is 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 is needed in French as the program is fully taught in English

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Course Description

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

Semester 1 Fall (October-January)

Fundamentals DSE I

  • Distributed Systems and Cloud Computing
  • Machine Learning and Intelligent System

Web science and mathematical methods

  • Image & Video Compression
  • Digital Image Processing
  • Information theory
  • Essential Mathematical Methods for Engineers
  • A hands-on approach to computer networking
  • Optimization Theory with Applications
  • Software development methodologies
  • Foundations of Statistical Inference
  • Interaction Design and Development of Modern Web Applications

Fundamental in Business, Innovation and Project Management (I)

  • How to adopt the right posture and move from idea to market!
  • Introduction to management
  • Responsible Digital Innovation: Risks, Ethics and Technology
  • Personal Development and Team Leadership

Language 1 (French, or another language if the student is already fluent in French)

Initiation Project (80 hours)

Semester 2 Spring (February-June)

Applications (I)

  • 3-D and virtual imaging (analysis and synthesis)
  • Iot Application Protocols
  • Cyber-crime and Computer Forensics
  • FormalMethods-Formal specification and verification of systems
  • Imaging Security
  • Machine Learning for Communication systems
  • Security applications in networking and distributed systems
  • Speech and audio processing
  • Semantic Web and Information Extraction technologies

Fundamentals I

  • Algorithmic Machine Learning
  • Advanced Statistical Inference
  • Deep Learning

Fundamental in Business, Innovation and Project Management (II)

  • Business Simulation
  • General introduction to law: contracts, setting up a business
  • Project management
  • Sociological Approaches of Telecom Technologies
  • Personal Development and Team Leadership

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

Semester Project

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, etc.). Students can work individually or in a 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)

  • Security and privacy for Big Data and Cloud
  • Image & Video Compression
  • Digital Image Processing
  • Mobile application and services
  • Optimization Theory with Applications
  • Statistical signal processing
  • Foundations of Statistical Inference
  • System and Network Security
  • Interaction Design and Development of Modern Web Applications

Fundamental in Business, Innovation and Project Management (III)

  • How to adopt the right posture and move from idea to market!
  • Introduction to management
  • Responsible Digital Innovation: Risks, Ethics and Technology
  • Personal Development and Team Leadership

Fundamentals DSE II

  • Distributed Systems and Cloud Computing

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

Semester Project Please see the description of the Project above (Semester 2) (100 hours)

Semester 4 Spring (February-August)

Research / Industrial Internship

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.

Last updated Jun 2020

About the School

EURECOM is a school that offers personalized support at every step of a student’s life, such as finding housing or dealing with administrative procedures. EURECOM benefits from a very good professor/ ... Read More

EURECOM is a school that offers personalized support at every step of a student’s life, such as finding housing or dealing with administrative procedures. EURECOM benefits from a very good professor/ student ratio with one professor to 8 students, which creates greater proximity. These ongoing exchanges between students and staff create strong ties that last beyond the time spent at EURECOM. Read less