Play A Linear Method for Non-Linear Work: Our Data Science Process
All data scientists have some sort of a process to move a project from initial customer contact through to successful completion. Often, this is not formally documented, though we can point out when we are being asked to take short cuts that process, even if we haven't described the steps to our customers.
In this talk, John Ehrlinger (Data Scientist, Algorithm & Data Science team) walks us through the 7 step process for data science detailing how data science works along the way. These 7 steps were developed in response to a customer request for insight into how a data science project would proceed in an effort to estimate time to completion. This process closely parallels more formalized methods with well-defined boundaries and goals and clear guidance on expected deliverables. We assume a linear approach toward project completion similar to waterfall method. However, we also understanding that data science is still science. An exercise in discovery, and allow returning to previously "completed" steps as new information is uncovered or becomes available.
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