Yanir reflects on how data scientists at Automattic work to improve customer retention.
Demet takes you deep into pipe, a tool that allows anyone at Automattic to build solid machine learning models.
Demet gives us an overview of pipe, Automattic's machine learning pipeline.
This week, Charles shares a couple of talks he enjoyed on neural networks at Georgia Tech's Theoretical Foundation of Deep Learning conference.
Check out Demet's data-driven analysis of communication at Automattic.
Don't forget to share your favorite reads in the data science field!
Recently, I was asked to determine the extent to which seasonality influenced a particular time series. No problem, right? The statsmodels Python package has a seasonal_decompose function that seemed pretty handy; and there's always Google! As it turns out, this was a bit trickier than I expected. In this post I’ll share some of the … Continue reading Investigating Seasonality in a Time Series: A Mystery in Three Parts