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.
… Continue readingThis 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.
… Continue readingLooker NYC Meetup
Like any company, Automattic is constantly on a journey to get better: sometimes we have the good fortune of finding improvement in leaps and bounds, but most of the time, we move slowly, we make small changes, finding iterative wins and moving down the to-do list. I think probably this is how most progress happens: … Continue reading Looker NYC Meetup
This Week in Data Reading: Experimentation, Tech and the Humanities, and Eliminating Bias in Testing
This week, Carly, Demet, and Charles bring you some interesting material on tech and the humanities, experimentation culture, and eliminating bias in testing.
… Continue readingThe 2019 Fairness, Accountability, and Transparency Conference
Charles Earl shares what he learned from this year's conference.
… Continue readingThis Week In Data Reading: MOOCs, Collusion in Artificial Intelligence, and Fake News
This week, Boris, Xiao, and Carly share recent reads about MOOCs, collusion in AI pricing, and generating fake news with artificial intelligence.
… Continue readingHow to Increase Retention and Revenue in 1,000 Nontrivial Steps
Yanir reflects on how data scientists at Automattic work to improve customer retention.
… Continue reading