How do you manage data science projects? Is it software? Is it research? Or maybe, simply magic? This four-part post is an overview 10 ways projects are or could be managed. To start, we’ll explore ad hoc project management, waterfall, and CRISP-DM.
Ironically, data science teams that are so intensely focused on model measurement often don’t measure their own project performance which is problematic because… …But wait! Data scientists measure all sorts of metrics. Of course, they will closely monitor data science metrics and KPIs such as RMSE, F1 scores, or correlation coefficients. Such metrics are critical […]
Traditional software approaches favor developing software layer-by-layer (horizontal slicing) while software agilists strive to deliver software by thin end-to-end value streams (vertical slicing). …but what makes sense for data science? Consider a churn project… Imagine that you are tasked to pro-actively minimize customer churn at a telecom company. The retention department has requested the following […]
What is Waterfall? Waterfall, also referred to as the classic life cycle or traditional project management, originated from manufacturing and construction and was applied to software engineering projects starting in the 1960s. A waterfall project flows through defined phases such as shown in the diagram to the right. Some waterfall models include variations of these […]
Ad hoc processes might work for smaller, one-off projects but are becoming less sustainable as data science matures into a team sport. Meanwhile, Waterfall is the classic highly-structured project management approach that dates back to antiquity and was common in software 10 – 20 years ago. Realizing the need for a process specific to data […]
