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.
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.
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, Carly, Demet, and Charles bring you some interesting material on tech and the humanities, experimentation culture, and eliminating bias in testing.
Charles Earl shares what he learned from this year's conference.
This week, Boris, Xiao, and Carly share recent reads about MOOCs, collusion in AI pricing, and generating fake news with artificial intelligence.
Yanir reflects on how data scientists at Automattic work to improve customer retention.