6 questions about the Master for Smart Data Science
What is ENSAI’s Master for Smart Data Science? How does the program manage to stay at the forefront of the ever-evolving fields of data science and AI? In less than 6 minutes and in 6 questions, François Portier, a professor of statistics and head of the Master for Smart Data Science, highlights the program’s strengths and the career opportunities awaiting graduates.
This one-year master’s program is taught entirely in English. It offers a solid foundation in data science and artificial intelligence, combining advanced concepts in statistics, optimization, and computer science.
Watch the video interview of François Portier, head of the Master for Smart Data Science
6 questions about ENSAI’s Master for Smart Data Science: read the full retranscription
What is ENSAI’s Master for Smart Data Science?
“My name is François Portier. I am an associate professor at ENSAI. I am the head of the Master’s in Smart Data Science program, a one-year program entirely taught in English. During the first semester, students complete six months of full-time coursework. After that, they undertake a four-to-six-month internship. The following September, they return to the school for their internship defense and the graduation ceremony.
This is a unique and innovative program in data science and artificial intelligence, combining advanced concepts in statistics, optimization, and computer science.
Who is this master aimed at? What are the application criteria?
We encourage both French and international students to apply. For example, each year, half of the students come from abroad. We also welcome candidates with professional experience.
During the application process, we require four years of higher education. We pay particular attention to candidates with a strong mathematical background as well as a strong background in computer science. All prerequisites are available on the program’s webpage.
What should candidates expect?
Each year, the program enrolls a limited number of students to ensure sufficient individual follow-up. At the beginning of the year, each student is lent a computer, and a dedicated program coordinator supports them throughout the year. I must admit that the program is quite intensive due to the significant number of lectures, which cover a wide range of topics, from IT tools to applied mathematics.
What are the key features that set this program apart from others?
One key feature is that students are trained to understand what lies behind algorithms. Algorithms should no longer be a black box for them. With this knowledge, they gain the ability to create and design their own algorithms when facing specific challenges.
A relevant example is the high-dimensional framework. We offer a dedicated course on this topic. When dealing with high-dimensional data, standard approaches are known to fail to achieve good performance. To address this issue, one solution is to build a statistical or probabilistic model to extract relevant information from the data.
This approach undoubtedly requires strong knowledge in mathematics, particularly in optimization and statistics.
Another key feature is the balance between theory and practice. For instance, Smart Data projects are organized each year. At the beginning of the year, several companies present problems or questions they wish to investigate. Students, working in groups, explore these questions with the support of the companies. They acquire practical knowledge about implementing algorithms in real-world situations.
How does this program remain state-of-the-art with the rapidly evolving field of data science?
Recently, machine learning methods have become increasingly popular across many scientific fields, and some are now well-established.
In designing our program, we carefully identified these methods. Take deep learning, for example. Its applications—such as image processing, image recognition, large language models, and time series analysis—are well known. The program includes several courses related to deep learning to provide students with extensive knowledge of both the theory and its applications.
These courses include deep learning, machine learning for time series, and a seminar on reinforcement learning. The goal of the program is to stay at the forefront of new technologies. Each year, the curriculum is updated with the help of teachers who are active experts in their fields.
Could you share some success stories of graduates?
There are many success stories among our graduates. One of them is a machine learning data scientist at Expedia and a co-founder of the Geneva chapter of Women in Data Science. Another former student is currently a postdoctoral researcher at L2S, a renowned lab in signal processing in Paris. He is working on neural networks defined on Riemannian manifolds.
Finally, let me mention one of our graduates who joined us from South Africa after leaving his job in a bank. He is now the Head of Data at Rocher.“
Do you want to become a data scientist? Learn more about ENSAI’s Master for Smart Data Science.