Olivier Matz- Winner of the 2020 DATA SCIENCE UL6S competition

December 07, 2020

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Olivier Matz

Capgemini - Artificial Intelligence Project Manager - Winner of the 2020 DATA SCIENCE UL6S competition

With a scientific background, I discovered data science during my PhD in materials science. In particular, my introduction to data science started with simple problems like finding patterns and correlations between material properties using machine learning methods. I continued my adventure in the world of data science by tackling more complex problems by applying deep learning methods to systems with too high a dimensionality to be treated by physical approaches. A true Artificial Intelligence enthusiast, but not only, I am also interested in underwater macro photography, travel and finance.

Data science being both a generic term and a vast field, my practice of the latter is essentially focused on the use and development of deep learning algorithms. In my daily work, I am required to manipulate different types of data such as time series, signals, images and text for different problems such as classification, detection or prediction. Being curious by nature, I spend a lot of my time exploring the different domains of deep learning: supervised and unsupervised learning, active learning, GANs, auto-encoders, set models and data fusion. It is mainly this same curiosity that led me to take part in the competition, both to discover the type of questions that are used to evaluate the level of a data scientist, and to assess my level of knowledge in data science compared to other enthusiasts from all over France.

The societal evolution that we have been experiencing since the beginning of the 2000's places data at the centre of our daily lives, and affects both our personal and professional lifestyles. In particular, the digitalisation of our daily lives has led to the creation of a massive flow of data. At the same time, the scientific and technological advances of the last decades have led to the development of electronic components and hardware capable of processing ever larger amounts of data (HDD, SDD, CPU, GPU, TPU, VPU, ...). Nowadays, the processing, exploitation and understanding of this data represents a major challenge for companies. However, although companies have become aware of the importance of data by digitising their processes and accepting data science in the sense of statistical processing of data, there is still a phase of acceptance of artificial intelligence, initially as a support for decision-making, and then as an autonomous decision-making system. Indeed, recent advances in the field of machine learning and deep learning have offered immense opportunities, particularly in industry for predictive maintenance, process optimisation and virtualisation.

Moreover, the data science community appears to be one of the most active in the world and defends the values of free sharing, both of code and of disruptive algorithmic advances. This is probably one of the reasons why data science has become so popular in recent years. At the same time, the development of tools such as scikit-learn, TensorFlow or PyTorch have made the use of data science accessible to everyone, making business expertise and good practices take second place to the use of tools. On the other hand, the proliferation of online training courses and certifications, while they offer good content and can be very useful for both theoretical understanding and tool use, are not sufficient to guarantee a certain level of operational skills and can damage the reputation of data scientists. In this context, I believe that it becomes necessary to develop an independent certification in data science and Artificial Intelligence to give credibility to data scientists and I welcome the initiative of UL6S in its project for an independent French certification in these fields.

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