Fresh data insights from Automattic.
Dive into our blog where we turn complex data into clear, actionable insights. Stay tuned for fresh perspectives and expert advice from the heart of Automattic’s data team.
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Five Paths to Automattic
Greg, Anna, Anand, Anna, and Robert — who come from a variety of backgrounds ranging from fullstack software development to linguistics, hardcore math, and more — share how they came to work with data at Automattic.
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Boris Gorelik on the biggest missed opportunity in data visualization
Boris Gorelik recently joined us to present on The Biggest Missed Opportunity in Data Visualization based on his recent talk at the NDR conference. Boris was a data scientist at Automattic, is now a data science consultant, and blogs regularly on data visualization and productivity. Some of highlights (along with a handy timestamp) include: Keep the…
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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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Gender and Racial Bias in Cloud NLP Sentiment APIs
Do natural language processing tools from Amazon and Google contain racial and gender bias? Charles Earl investigates.
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Christo Wilson Discusses the Ethics of Online Behavioral Experiments
Charles Earl shares the insights he gleaned after hearing a talk from Dr. Christo Wilson about ethics in online behavioral experiements.
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Using ML for Campaign Optimization: Our Journey to Marketing Science at Automattic
Demet on using pipe to segment and study how users respond to marketing email campaigns.
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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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Looker 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:…
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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.
Data for Breakfast 