La prolifération de données dans divers domaines d'application a abouti à l'émergence de la discipline science de données (SD) dont l'objet est d'extraire des connaissances, ou de la valeur, à partir de données. L'objectif de cette formation est de former des ingénieurs et des chercheurs en science de données en capacité de comprendre et de concevoir des solutions applicatives à des problèmes relevant de différents cycles de vie de la donnée.
Pédagogie
AIMS
The proliferation of data in various application domains has led to the emergence of the discipline of data science (DS) whose purpose is to extract knowledge, or value, from data. The objective of this program is to train engineers and researchers in data science who are able to understand and design application solutions to problems related to different life cycles of data. In particular, they will develop skills in:
Collecting large amounts of data and storing it
Data analysis, information retrieval and knowledge extraction
Reasoning about this knowledge for decision making and value generation.
Application cases from strategic domains, such as smart cities, health and transportation, will be studied and addressed.TARGETED STUDENTS
Have a bachelor's degree in computer science, mathematics-informatics or equivalent.
LEARNING SITES
- SCIENCES, Marseille St-Jérôme
LEARNING AND RESEARCH
This course is supported by the scientific landscape of the Computer Science and Systems Laboratory, both by teams from the Data Science cluster (DIAMS, DANA, R2I and TALEP) and the Computing cluster (COALA, LIRICA and MOFED).
KNOWLEDGE TO BE ACQUIRED
In the 1st year: software engineering, complexity, data science, concurrent programming, networks, algorithms and OR, communication, data quality and visualization, post-relational data, JEE architecture, probabilistic aspects for computer science.
In the 2nd year: methodology for data science, data mining, information retrieval and recommendation, big data, reasoning in uncertainty, semantic web, ontology and reasoning, cloud computing. Additional courses will be offered to alternating students: Graph analytics, Business intelligence platforms and data analysis
PROFESSIONAL SKILLS TO BE ACQUIRED
- Analyze data science problem situations
- Integrate, store and query data in centralized or distributed systems
- Use service-oriented and software architectures for application development
- Prepare and present data in a way that facilitates decision making
- Use knowledge representation and reasoning formalisms
- Analyze and process large amounts of heterogeneous data
- Master and deploy data science methodologies
- Master data mining and information retrieval tools
- Identify personal data and respect confidentiality rules and proceduresINTERNSHIPS AND SUPERVISED PROJECTS
A pedagogy based on projects and internships in the 1st year (3 to 5 months) and 2nd year (5 to 6 months) is implemented. They can be carried out in a company or in a research laboratory.
SPECIFIC TEACHING CONDITIONS
The training is accessible through apprenticeship or professionalization contracts.
LEARNING COURSES LIST
Inscription
ADMISSION CONDITIONS
Access to M1 is conditioned by a bachelor's degree in computer science, mathematics and computer science or equivalent.
Access to the M2 is conditioned by a level of M1 in computer science or equivalent.
SCHOOL REGISTRATION
Typical courses can be access by- Initial Formation
- Alertnating Learning formation
