By Eugene Yan
We close the loop with Lesson #1: Learn better by doing. I’ve shared my hard-won lessons from interviews with experts, observing other data scientists, and my own mistakes. That’s as far as I can go—now, it’s up to you to apply them.
“What’s the most important skill for a data scientist?”, I asked my mentors. Was it ninja coding abilities? Academia-level research skills? In-depth machine learning know-how?
In 2014, Google published the paper “Machine Learning: The High-Interest Credit Card of Technical Debt”. In 2015, they follow-up with “Hidden technical debt in machine learning systems” at NIPS. Together, both papers have been cited more than 400 times.
Amazon is one of the most successful companies in the world. It started as an online bookstore and has transformed itself into the world’s largest retailer. It’s also the market leader in cloud and is a giant in film and television.
In “The Lean Startup”, Eric Ries shares how small teams, with limited resources, can quickly learn what works: Build a minimum viable product (MVP), test, iterate. He called this the “Build-Measure-Learn” feedback loop. To build an MVP, he advised to “remove any feature…
When I was starting in data science, I devoured online courses, videos, and articles. (My degree was in Psychology, so I had a lot of catching up to do!). These lessons were well taught—after all, the teachers were top professors and writers and were considered the best in the…
Hi, nice to meet you. I’m grateful you’ve signed up to stay in touch, and I hope this newsletter can help you in your data, ML, or career-related endeavors. First, a bit about myself and this newsletter.
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