Big Data is a strategic priority for businesses of all sizes and sectors. It is a revolution that is bringing sweeping changes to companies and is seen as an essential tool to create value.

Télécom Paris Tech leads three teaching and research chairs related to Big Data, offers a wide range of initial and continuing training programs, and promotes innovation through its incubator, Télécom Paris Novation Center. This brings together approximately fifty research professors, fifty PhD students and about one hundred graduates per year.

The training aims to acquire scientific bases in artificial intelligence and robotics, a general culture in cognitive sciences and neurosciences for the "intelligent" and "bio-inspired" processing of information: 

• neural network models, 
• optimization algorithms, 
• new techniques for man-machine interface, 
• embedded computing and bio-inspired robotic control.

As the field of Data Science evolves quickly, ENSAI is taking advantage of the reaccreditation process to update the Master in Statistics for Smart Data program which will now be known as the Master for Smart Data Science.

MHD Modelling School brings together professional lecturers, PhD students and open-source simulation software users from the fields of applied magnetohydrodynamics (MHD) and induction heating of metals. It is an intensive hands-on course with the focus on contemporary tools modelling tools for industrial processes. The course also covers experimental methods used for verification of numerical models. Hands-on sessions showcase open-source simulation software: Elmer, GetDP, OpenFOAM, EOF-Library.  Pre- and post-processing packages Salome and ParaView are introduced.

The course provides basic knowledge in the programming of distributed memory architectures using the Message Passing Interface (MPI) standard. After presenting the basic concepts of high-performance computing and the main areas of their application, the methods for evaluating the efficiency and characteristics of sequential and parallel computing are considered. The following is a presentation of the basics of the MPI standard and its application for creating portable parallel programs. Some profiling and adjustment techniques are also discussed.

We welcome all doctorate, master's, and curious 3rd-year students interested in the frontier between neurosicence and computer science. This autumm we will cover the neural basis of representation of space, and how this drives innovations in artificial intelligence.

Parallel and distributed systems are ubiquitous in many applications in our daily life including AI, online games, social networks, web services and healthcare simulations. These systems distribute computation over many computing units because they have to sustain massive workloads that cannot fit into a single computer. Designing efficient, easy-to-maintain and correct parallel and distributed systems is challenging – a challenge inherent to the complexity of managing multiple machines, many users and very large data sets.

This international master track titled 'Performance in Software, Media and Scientific Computing' or PSMSC is a one-year programme taught by the University of Toulouse. It prepares master students for top level careers or PhD programs in several application domains that require use of hardware, software and programming techniques to accelerate data-intensive computations and ensure reliable execution of cutting-edge applications.

undamentals of artificial intelligence 

• Ethics and deontology in artificial intelligence 
• Logic and symbolic artificial intelligence 
• Statistics 
• Probabilistic graphical models 
• Basics of statistical learning 

Learning and optimisation for AI 

This course aims to give the students a practical approach to IT systems by putting them into the role of a system administrator. During this course the student will try to shoulder different tasks a normal system administrator has to face daily, while learning about systems, integrations, monitoring and DevOps.

Data is everywhere, increasingly large and complex, coming from various sources such as computers, mobile devices, sensors and social networks. This data is creating new needs and emerging applications that require advanced techniques related to various domains such as Big data, semantic web, internet of things, analytics and data science etc.  It is about managing large, dynamic and varied data that can be linked by complex relationships. The classic functionalities of traditional enterprise information systems are inadequate and insufficient to meet these needs effectively.

This course aims to provide computer scientists with a specialisation in the field of digital imaging. Its particular interest is to deal with : - the design and implementation of image-based applications (production, analysis and transmission of images, virtual reality, 3D animation). - the development of solutions dealing separately or simultaneously with image analysis and synthesis, 3D reconstruction, animation, virtual reality, augmented reality, with the help of artificial intelligence tools.

The aim is to train professionals with advanced skills in computer science, optimisation and data science for the valorisation of data deposits.

The training will address the technical and legal aspects, thus meeting the expectations of the following professions: data scientist, data engineer, data manager, data analyst, project manager, decision support engineer, quality control manager.

The originality of the training is to address a broad spectrum of skills in computer science, data science and optimisation.