Lino Galiana

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I am data scientist at the French National Institute of Statistics (Insee). My work focuses on leveraging new data sources and innovative methods to develop reliable statistics on socioeconomic phenomena. I have a particular interest in the analysis of textual and spatial data.

I mostly work with Python, and Javascript. Most of my projects, both personal and professional, are available on my GitHub page or the GitHub page of Insee’s Lab. A selection is featured in the projects section below, including a few inspired by my love of cycling 🚲. I also teach a few courses, more on that in the teaching section.

Teaching

I currently teach Python for Data Scientists in two of France’s top engineering schools (at ENSAE Institut Polytechnique and ENSAI). You can find the course materials on the GitHub repository .

I also teach a course on Data Science Projects in Production at ENSAE. This course addresses state-of-the-art MLOps practices, with materials available on the GitHub repository .

I have also developed several other courses in the past:

• Good practices in R and Git
• An introduction to (GitHub repository ), taught as an introductory data analysis course at Ecole Normale Supérieure
• A course on new data sources, now taught by Insee colleagues.

I also gave the following courses in the past:

• 2019-2021: Macroeconomics (Bsc)
• 2016-2019: Urban Economics (Msc), Sciences Po
• 2016-2017: Mathematics for Economics (Msc), Sciences Po
• 2016-2017: Microeconomics (Msc), Sciences Po

Open source projects

Among my main open source projects, I maintain utilitR, a collective, community-driven documentation effort for the software within the French administration, and cartiflette, which gives data scientists easy access to France’s official geographic boundaries for mapping.

I also enjoy turning my professional tools toward personal projects — climbtracker ⛰️🚲 is one such example, helping road cyclists find nearby climbs.