Last week, a stereotypical “French” ceremony opened the 10th Rugby World Cup in Stade de France, in the suburbs of Paris, France. As a small boy growing up in the southern half of France, I developed a strong interest for the sport. Now being an adult living and working in North America, where barely anyone has ever heard the word “Rugby”, I now rarely have anyone else to talk to about Antoine Dupont’s (captain of the French team and best…
tl;dr Trying to debug a poorly performing machine learning model, I discovered that the distribution of one of the features varied from one date to another. I used a simple and neat affine rescaling. This simple quality improvement brought down the model’s prediction error by a factor 8 Data quality trumps any algorithm I was recently working on a cool dataset that looked unusually friendly. It was tidy, neat, interesting… the kind of things that you rarely encounter in the wild!…
Being able to make causal claims is a key business value for any data science team, no matter their size.Quick analytics (in other words, descriptive statistics) are the bread and butter of any good data analyst working on quick cycles with their product team to understand their users. But sometimes some important questions arise that need more precise answers. Business value sometimes means distinguishing what is true insights from what is incidental noise. Insights that will hold up versus temporary marketing…
“Tiens, Pâques est tard l’année prochaine”. Vous avez peut-être aussi déjà entendu cette phrase à propos de Pâques 2017 ? Il est vrai que l’année prochaine, le dimanche pascal aura lieu le 16 avril, soit 20 jours plus tard qu’en 2016 (27 mars). Je me suis demandé quelle était la distribution des dates de Pâques, et quand on pouvait considérer que “Pâques tombe tôt/tard cette année” Wikipedia nous informe que “Pâques est le dimanche qui suit le 14e jour de la Lune…