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4 Project Management Skills and Frameworks for Data Scientists
In this post we’ll learn 3 project management frameworks and 1 skill that help data scientists steer projects towards success
Most data science projects are time fixed ventures with limited budget. While it’s healthy that data science projects have some loose “free-time” due to their scientific nature, the fact that resources (time, money or people) are limited, justifies that data science projects have to be planned carefully. In the end, you want to make stakeholders happy and good planning sets expectations and controls the project execution.
This is specially relevant for consulting gigs where drifts from the original plan may be catastrophic. Financial incentives must be aligned regarding the project implementation and any over-budget or incomplete projects normally spawn from bad planning.
While it is true that data science projects have a likelihood of failing, the reasons for that failure should fall back on the statistical hypothesis, not on a lack of planning.
And, yes — it’s not the main responsibility of a data scientist to manage projects from top to bottom, but learning some frameworks will definitely help in understanding certain project decisions and participate…
