Honestly, I’m here to share a pain point. As a former DataScientist, I used to do a lot a google search for poor reasons …

“How do you spell this package? What is the method I’m looking for? Oh, this small problem … I already dealt with it, where was the website where I found the solution?”

This is the reason why I started to create my own documentation to store all the solutions/ things that I often use in the same place. With Alaa Bakhti, we created, both on our side, a sphinx documentation. But the main reason for…


Data Science in our daily life is clearly increasing and we can see a lot of new use cases appearing every day. And if I should define it, I’ll say that Data Science is at the crossroad between Mathematics, Physics and Computer Science; It aims at revealing patterns in data and using them to produce new insights to help people decide.

As I said upper, Data Science is something moving daily so how should I hope for a better transition to move on to the topic of Time Series Analysis?

This article aims to make people aware about data leakage…


Time series are numerical series representing the evolution of non-static specific phenomena through time such as in finance, weather, or in the industry.

Analyzing and making predictions of time series has become an important stake in data science. Anomalies detection classifies very rare events as being different from a ‘normal’ behavior. It becomes a major problem in time series analysis and it can be solved by supervised or unsupervised binary classification algorithms. Something to keep in mind, this is an unbalanced problem: anomalies account for less than 1% of observations and class unbalance has a deep impact on metrics. …

Yann Hal

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