LinkedIn: Producing daily content about statistics and causality to fight misinformation (center of all my activities) [LINK]

Medium (Towards Data Science): sharing my key tips in Data Science (with Python applications) [LINK]

YouTube channel: teaching key concepts in statistics, popularize scientific research, academic life (tips and insights) [LINK]

Instagram: explaining how to question causality and how to measure properly a causal effect without math, but with a ton of memes. [LINK]

Teaching: class for any level at university, public-speaking and events

Towards Data Science (Medium)

I use this platform to share all my tips to work and analyze data for scientific research as well as in the "real" world. My first article is "A recipe to empirically answer any question quickly" shows how I do Exploratory Data Analysis in Python with a concrete and timely example: The effect of heat-shock on environmental policies. [LINK]


On this channel, you'll find information about two main topics: inferential statistics and academic career (phd and beyond).


The first mission of this channel is to popularize concepts in inferential statistics without math to empower you against misinformation or manipulation. This is more important than ever, in an era where numbers and scientific research is used everywhere.


The second main topic on this channel is the life as a researcher in academia. I'll present the daily life, the challenges, advices on "how to get to a PhD program?", "what about after the PhD?" etc.


Every choice you make every day are based on the assumptions of some causal effect. If I drink just one glass with friends tonight, I’ll be fine. If I eat local food, I’ll pollute less.

The trick is: it’s often incredibly hard to identify a causal effect. And, hence, many people, some politicians, news reporters, or even doctors will rely on a correlation, pretending or thinking that it’s causal to take a decision, conclude something or give advice, which is very problematic.

So, to live in this world, and prevent misinformation/manipulation, you require to have basic knowledge of statistics and causality!

The first thing we must be clear is that correlation does not imply causation.

For example, during summer more people are using sunscreen and more people are drowning. That doesn’t mean at all that one causes the other. It’s just a correlation. So, if you ban sunscreen to prevent people from drowning, that might not be very efficient and clearly even worse for the population.

We’ll see together how to question causality and how to measure properly a causal effect without math, but with a ton of memes. Cheers!


Over the last 10 years, I taught statistics to more than 10,000 students from bachelor in economics to in-vivo lab research passing by managers. I'm available to

List of class at university: Statistics I and II, Empirical Research, Data Science and Statistics, Economics for challenging times, biostatistics, Applied Statistics and Econometrics, Statistics and Econometrics II, and Microeconometrics, Microeconomics 101.