SciencesPo

My conviction is simple: innovation only has meaning when it serves humanity and the collective good. It is with this in mind that I accepted a position as a lecturer at Sciences Po Paris, where I led a practical workshop integrated into the course OMIN 2220 – Big Data: Elementary Tools and Value Creation.

There, I guided students through a concrete yet structured exploration of Data Science and Artificial Intelligence. We worked on the foundations: the difference between supervised and unsupervised algorithms, understanding models, and manipulating essential tools. Students were introduced to key machine learning techniques, linear and logistic regression, SVM, hidden Markov models, and an introduction to deep learning (not as a list of algorithms to memorize, but as building blocks for creating value).

Through practical case studies and presentations, we explored how these technologies address real-world challenges: the mechanics of startup creation, practical issues, product design, and operational impact. The goal was not simply to master the technology, but to grasp its scope: how AI can become a lever for informing decisions, amplifying human projects, and transforming ideas into solutions.