Author Archives: Aaron Beach

About Aaron Beach

Aaron Beach is a data scientist with experience in email services, energy systems, privacy, social networks, mobile apps, natural language processing, recommendation systems and Big Data. He has a PhD in Computer Science and has published over 25 research papers.


Articles Posted by Aaron


SendGrid Shares Data Around European Email Opens

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In August, we celebrated the 32nd anniversary of email by releasing some unique email statistics from our data set here at SendGrid. We shared email open rates by device and region and predictions on the future of app-generated email, spam filtering, and email quality. Now, we’d like to share some more specifics focused on email opened in Europe. Collected over two 10-day periods in 2013 and 2014, this SendGrid data set spans more than 8 billion emails sent by over 125,000 companies. Here are some highlights from the data shared by our team: Tablet usage for emailing continues to increase

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Data Scientists, Think More Strategically

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There’s a funny anecdote, repurposed throughout the years, originally about the difference between Soviet and American military design during The Cold War. The anecdote relates how the Americans and Soviets agreed to end The Cold War once and for all to avoid nuclear war. Each country would have 5 years to produce the ultimate fighting dog after which The Cold War would be settled with a dog fight. The Soviets bred fierce Rottweilers and Dobermans with Siberian wolves using genetic engineering and competition for resources. Finally a single ferocious fighting dog was produced the likes of which the world had

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Data Scientists, Beware Your Own Arrogance

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Before joining SendGrid as the Senior Data Scientist, I had a chance to witness a lot of big data research, in both academia and industry. I have observed a number of pitfalls, which I hope to avoid myself, and would like to share them with you. This post is about arrogance, an easily-manifested trap for any data scientist. Fundamentally, the techniques and technologies employed by data scientists are not new. What’s new is the confluence of understanding techniques and the ability to implement them in software quickly, allowing algorithms and data structures to be applied to new domains in a

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