Filmed at dotAI on October 18, 2024 in Paris. More about the conference on https://www.dotai.io In data science, the glory is in the AI, the machine learning, but the hard work is often cleaning, wrangling, preparing the data. This is particularly true when working on data tables, as opposed to text or images that have more invariants across tasks. I will present how to reduce this burden, with the young library "skrub", as well as ongoing research. Who is Gael Varoquaux? Gaël Varoquaux is a research director working on data science at Inria where he leads the Soda team. He is also co-founder and scientific advisor of Probabl. His research covers fundamentals of AI, statistical learning, NLP, causal inference, as well as applications to health. He also co-funded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python.