I am data scientist at the French national statistical institute, Insee. I study how emerging data or new computational methods help to renew the production of statistical knowledge.
I mostly work with Python
and . I sometimes use C++
to improve performance
or Spark
for big data analysis.
I am a huge Git
fan.
I also like a lot
the possibilities offered by state-of-the-art data-science tools to
reduce the cost of exploring new datasets or new questions.
Most of my work is available on my Github page or
the Github page
of the Insee’s Lab.
I maintain the utilitR
project which is a collective effort involving many people from French administration to propose a high-quality documentation regarding software.
See Projects 👇 and Publications 👇 and Talks 👇 sections for more details.
I currently teach Python for Data Scientists
(Github repository )
at ENSAE Paris Tech
,
one of the top French engineering school.
I also teach a course “Reproductibility and good practices in data science projets”
(Github
repository )
that brings student to the question of MLops.
I used to teach urban economics at Sciences Po Paris and
macroeconomics for candidates to the Insee exam. See Teaching 👇 section for more details.
Msc Statistics and Data Science, 2017
ENSAE
Msc Econometrics, 2013-2018
ENS Lyon & Paris School of Economics
Msc Applied Mathematics, 2015-2017
Université Pierre et Marie Curie (Jussieu), Paris VI
Past courses:
List of some courses I gave recently
Cours en 2e annĂ©e d’ENSAE
(M1) disponible sur https://pythonds.linogaliana.fr/
Ce cours donnĂ© en dernière annĂ©e de l’ENSAE
et construit avec Romain Avouac est disponible sur le site web https://ensae-reproductibilite.netlify.app/ (dépôt ).
R
et Git
Cours pour dĂ©couvrir la manière d’utiliser R
dans un projet collaboratif avec Git
crĂ©Ă© Ă l’Insee
avec Mathias André, Romain Lesur, Annie Moineau et Olivier Meslin.
Le contenu du cours est disponible sur le site web https://collaboratif-git-formation-insee.netlify.app/. Le code source est disponible sur le compte Github
InseeFrLab
Undergraduate macroeconomics course for exam preparation at INSEE
Urban Economics course at Sciences Po. Program available here
Undergraduate mathematics course at Sciences Po
Undergraduate microeconomics course at Sciences Po
A list of open-source or research projects I participated in
I explore the evolution of segregation in the three French biggest cities within a typical day using individual mobile phone data combined with traditional data sources. I propose an innovative methodology to build within-day segregation indices and study segregation dynamics at fine spatial granularity
Les rĂ©sultats principaux de l’Ă©tude sont les suivants:
Plus de détails ci-dessous ! 👇
We bring together mobile phone and geocoded tax data on the three biggest French cities to shed a new light on segregation that accounts for population flows. Mobility being a key factor to reduce spatial segregation, we build a gravity model on an unprecedent scale to estimate the heterogeneity in travel costs.
Residential segregation represents the acme of segregation. Low-income people spread more than high-income people during the day. Distance plays a key role to limit population flows. Low-income people live in neighbourhoods where the spatial frictions are strongest.
Some blog posts I wrote about data-science, statistics or informatics