Master of Science in Data Science

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 that 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.

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)

Foundations in Machine Learning

• Machine Learning and Intelligent Systems

Foundations in Data Science

• Distributed Systems and Cloud Computing
• Database Management System Implementation

Computer Science for systems

• Image & Video Compression
• Digital Image Processing
• Quantum Information Science
• Software development methodologies
• Interaction Design and Development of Modern Web Applications

Humanities and Social Sciences 1

• How to adopt the right posture and move from idea to market!
• The challenges of a sustainable economy
• Introduction to management
• Intellectual property law
• Responsible Digital Innovation: Risks, Ethics and Technology
• Personal Development and Team Leadership

Scientific and technical opening 1

• Advanced topics in wireless communications
• Awareness-raising to research
• Computer architecture
• Digital communications
• Emulation and simulation methodologies
• Image & Video Compression
• Digital Image Processing
• Essential Mathematical Methods for Engineers
• Mobile communication techniques
• Mobility Modeling
• Mobile application and services
• Mobile communication systems
• Mobile Systems and Smartphone Security
• Operating systems
• Optimization Theory with Applications
• Quantum Information Science
• Statistical signal processing
• Secure communications
• Software development methodologies
• System and Network Security
• Designing embedded systems with UML
• Design and Development of Modern Web Applications

Language Unit ECTS: 1.00

Semester 2 Spring (February-June)

Advanced in Machine Learning

• Algorithmic Machine Learning
• Advanced Statistical Inference
• Deep Learning

Humanities and Social Sciences 2

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

Mathematical Tools and Web Science

• 3-D and virtual imaging (analysis and synthesis)
• IoT Application Protocols
• Advanced Statistics
• Formal methods-Formal specification and verification of systems
• Imaging Security
• Machine Learning for Communication systems
• Iot Communication Protocols
• Speech and audio processing
• Semantic Web and Information Extraction Technologies

Scientific and technical opening 2

• 3-D and virtual imaging (analysis and synthesis)
• Application Protocols
• Advanced Statistics
• Awareness-raising to research
• Computational Methods for Digital Communications
• Digital systems, hardware-software integration
• Cyber-crime and Computer Forensics
• formal methods-Formal specification and verification of systems
• Hardware Security
• Imaging Security
• Information Theory
• Introduction to statistics
• Machine Learning for Communication systems
• Mobile Advanced Networks
• Wireless Access Technologies
• Network Softwerization
• Transportation Planning
• Communication Protocols
• Radio engineering
• Projet de recherche
• Signal Processing for Communications
• Speech and audio processing
• Emission and Traffic Efficiency
• Semantic Web and Information Extraction Technologies
• Wireless Security

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

Semester 3 Fall (October-January)

Applications in data science

• Image & Video Compression
• Digital Image Processing
• Mobile application and services
• Optimization Theory with Applications
• Quantum Information Science
• System and Network Security
• Interaction Design and Development of Modern Web Applications

Cloud Security and Blockchain

• Security and Privacy for Big Data and Cloud
• Multiparty Computation and Blockchains

Humanities and Social Sciences 3

• How to adopt the right posture and move from idea to market!
• The challenges of a sustainable economy
• Introduction to management
• Intellectual property law
• Responsible Digital Innovation: Risks, Ethics and Technology
• Personal Development and Team Leadership

Scientific and technical opening 3

• Advanced topics in wireless communications
• Awareness-raising to research
• Computer architecture
• Digital communications
• Emulation and simulation methodologies
• Image & Video Compression
• Digital Image Processing
• Essential Mathematical Methods for Engineers
• Mobile communication techniques
• Mobility Modeling
• Mobile application and services
• Mobile communication systems
• Mobile Systems and Smartphone Security
• Network Modeling
• Operating systems
• Optimization Theory with Applications
• Quantum Information Science
• Statistical signal processing
• Secure communications
• Software development methodologies
• Standardization activities
• System and Network Security
• Designing embedded systems with UML
• Design and Development of Modern Web Applications

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…). Students can work individually or in groups of 2/3. The expected workload is 200 hours of individual work per semester. A defense is organized at the end. Projects provide students with hands-on skills by allowing them to put concepts into practice. (200h)

Semester 4 Spring (February-August)

Research / Industrial Internship

The internship is to be carried out in a company or lab in France or abroad. It allows students to get hands-on experience and ease their entry into the job market. 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 helps students find an internship by providing an updated database of paid internship opportunities offered by companies.