DSLP - The Data Science Project Management Framework that Transformed My Team

Because, Data Science is fundamentally an R&D project, so there is no concept of an end-product that you are trying to build at the start. Research is required to determine what the end-product might look like.

Only after the R&D is finished, and you know what data you need, what preprocessing/feature engineering is required, and what model you are going to use, do you finally know what you are going to build.

This means that the agile framework only becomes applicable when you are trying to productionize your model, which for a Data Science project, is the very last step of the project.

After researching the field of Data Science project management, the author came across the Data Science Lifecycle Process which he feels seems to encompass all the key insights that other resources provided into one framework that can be incorporated directly into Github projects or any other Kanban-based project management tool.