Current context of Data Sciences

The "data sciences", in practice for companies, combine :

• different mathematical domains, firstly statistics and machine learning, but also signal processing, computer vision, recognition of forms, and more, all contributing to artificial intelligence,
• and different areas of information processing in computer science, such as big data - be it storage, high-performance computing, visualization - programming in natural language or physical measurements.

It is a matter of making the transition from data to knowledge, if necessary by treating the creation of the data itself.

Depending on the application, data is either structured or unstructured, is derived from text, images, measurements or evaluations; different types of pre-treatment and learning can be considered: supervised, unsupervised, reinforced, etc. A wide variety of data science subjects arise from this diversity.

Kannon MSD has a natural fundamental capability, and the two guiding principles that can be seen in the name "Modelling-Simulation-Decision" :
Capacity : KMSD is an spin-off from the Centre for Mathematics and Their Applications (CMLA), a joint ENS Paris-Saclay and CNRS research unit . This centre brings together teams of the highest international level in big data and machine learning, image processing, and physical simulation based on high-performance computing. It was created 20 years ago and has since run the data sciences Master’s course of reference in France (MVA, Master Datasciences at University Paris Saclay). Kannon MSD is naturally an actor with the same scientific and technical rigour, being aware of the state of the art, while delivering results and tools that meet the expectations and constraints of its corporate clients,
1st guideline : the challenge of converting data to knowledge in companies is to be able to make informed decisions that are optimized in relation to objectives, and enable effective implementation. KMSD always maintains the desired objective as a structural element of its approach ; experience has taught us that this is decisive in defining a work program, and the methods and tools to be employed,
2nd guideline : Success depends on ability, by strictly sticking to what is necessary, taking the state of the art in the targeted field, and using already mastered modelling and simulations, at the level of the relevant approach. KMSD, both by its own resources and by its network of experts, has precisely the corresponding powers and means.

KMSD Data Sciences are thus the path from data to knowledge, then from knowledge to decisions, and finally from decisions to results for corporate clients.