In a new post, machine learning engineer Eugene Yan shares hard-won lessons on starting data science projects. Based on his own experience, he offers practical guidance for the early stages of a project, where many outcomes are decided.
The post focuses on how to approach the initial phase—what to consider, what to avoid, and how to set up for success. Yan draws on lessons learned from real projects, making the advice concrete and actionable.
While the post is specifically about quick-starting, the underlying themes apply broadly to data science work: preparation, clarity, and iterative learning. For practitioners, it's a useful reminder that the beginning of a project often shapes its trajectory.