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How to Avoid Statistical Bootstrapping Pitfalls
Yanir Seroussi shares some insight into the common pitfalls of statistical bootstrapping and how to avoid them.
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Data Science Insights from Cameron Davidson-Pilon
Cameron Davidson-Pilon talks to the Automattic data scientists about his work post-Shopify, data practices, and how data scientists can best serve their organizations.
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This Week in Data Reading: Project Estimation, Abandoning Significance, and Video Games.
This week, Rob suggests using statistics to help you plan your next project, Carly shares some surprising use cases for artificial intelligence, and Boris imagines a world without significance testing.
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How to Increase Retention and Revenue in 1,000 Nontrivial Steps
Yanir reflects on how data scientists at Automattic work to improve customer retention.
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Building Thousands of Reproducible ML Models with pipe, the Automattic Machine Learning Pipeline
Demet takes you deep into pipe, a tool that allows anyone at Automattic to build solid machine learning models.
Data for Breakfast 