What’s it that makes an information science project stand out? At a time when job applicants are expected to have shiny portfolio sites and when data teams must display their business value on an ongoing basis, the query has rarely been more crucial.

Technical ability is a serious ingredient, after all, but as editors we regularly realize that it isn’t enough. Probably the most compelling project-based articles we share with our readers actually showcase their authors’ know-how and expertise, but much more vital, they display an understanding of scope. The issues they tackle could be theoretical or practical, work-related or entirely passion-driven; no matter topic, though, we never lose sight of what they aim to do (and what they don’t) and of how each step along the best way takes us closer to an answer.

Somewhat than go on and on concerning the characteristics of great data science and machine learning projects, we invite you to explore some excellent ones for yourself—we’ve chosen a robust lineup of recent project walkthroughs where you could find inspiration, guidance, and maybe even a practical roadmap for developing your personal ideas.

Before we dive in, we desired to thanks, as at all times, for all of your support. For those of you who’d wish to make a meaningful contribution, consider becoming Medium members.

TDS Editors