After the first two years of theoretical and operational studies in statistics, computer science and economics, engineering students opt for a year of specialization during which their expertise in data science takes on a professional dimension.
Data Science & Statistical Engineering
This specialization reinforces the student’s knowledge of statistical modeling, covering topics such as quality and reliability, image and signal processing, as well as forecasting and its applications. Students are thus able to adapt to problems in a wide range of sectors, including industry, banking, the environment, digital services, etc.
Data Science & Risk Management
The aim of this program is to train data scientists specialized in risk management and quantitative finance. They are able to innovate and propose new methods of analysis. Specialization is organized around three main areas of expertise: banking risk management and regulation, allocation and investment strategies, and innovation in financial engineering.
Data Science in Healthcare & Biostatistics
This specialization trains students to become biostatisticians and data scientists in the healthcare field. It draws on complementary statistical and machine learning skills to provide the tools needed to design studies and analyze data from the experimental sciences. In particular, courses in epidemiology, clinical trials and Omics (genomics) data analysis provide students with solid training for applications in the healthcare sector.
Data Science & Engineering
The Data Science & Data Engineering specialization aims to train engineers to master the tools of artificial intelligence and their implementation on large databases. The training offered reinforces students’ knowledge of computer science for data analysis and management, as well as the latest tools for statistical learning.
Data Science & Marketing
Faced with the massification of data, companies need analysts capable of transforming it into relevant information that will ultimately optimize conversion rates and, more generally, return on investment (ROI). This specialization aims to train data scientists with both a very strong understanding of marketing business issues, within the ethical and regulatory framework of the RGPD, and very high analytical and quantitative skills.
Data Science for Economics and Finance – Economics Track
This specialization provides training in advanced econometrics and applied economics, using data science and machine learning tools to analyze markets, evaluate public policies, and inform decision-making in key areas. The Data Science for Economics and Finance track – Economics track opens the door to a wide range of career opportunities in economic decision-making, in both the public and private sectors.
Data Science for Economics and Finance – Finance Track
The goal of this track is to train engineers capable of mastering economic and financial theory, rigorous statistical and econometric methods, and the latest computational tools. The growing role of machine learning and artificial intelligence in asset management, valuation, and risk analysis further reinforces the need for professionals with strong expertise in all three of these areas.
Data Science & Advanced Machine Learning
This specialization deepens students’ knowledge of machine learning, particularly regarding current high-stakes issues in artificial intelligence: image processing, model interpretability, and trustworthy artificial intelligence. Students are prepared to tackle cutting-edge industrial and research challenges.