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Data Science Project Management
A 5-step Project Management Framework for Data Science
This article is part of a larger series on Full Stack Data Science. In the previous post, I introduced the idea of a full-stack data scientist and the 4 hats it entails. In this article, I will discuss the first of these four hats — the project manager (PM). While there are countless ways to approach data science project management, here I propose one possible framework and the PM’s role in executing it.
Data science projects often involve developing machine learning (ML) models to solve business problems. While this may seem commonplace in business today, it still comes with several risks.
Namely, developing ML models is inherently uncertain, technically demanding, expensive, and time-consuming. These risks motivate project management frameworks specifically designed for data science projects in mind.
Here, I will describe one such approach and break down the key contributions of a project manager in this context.
