What is it that makes a data science project stand out? At a time when job applicants are expected to have shiny portfolio sites and when data teams need to demonstrate their business value on an ongoing basis, the question has rarely been more crucial.
Technical ability is a major ingredient, of course, but as editors we often realize that it isn’t enough. The most compelling project-based articles we share with our readers certainly showcase their authors’ know-how and expertise, but even more important, they display an understanding of scope. The problems they tackle can be theoretical or practical, work-related or entirely passion-driven; regardless of topic, though, we never lose sight of what they aim to do (and what they don’t) and of how each step along the way takes us closer to a solution.
Rather than go on and on about the characteristics of great data science and machine learning projects, we invite you to explore some excellent ones for yourself—we’ve selected a strong lineup of recent project walkthroughs where you can find inspiration, guidance, and perhaps even a practical roadmap for developing your own ideas.
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