In designing a data science project, establishing what we, or the users we are building models for, want to achieve is vital, but this understanding only provides a blueprint for success. To truly deliver against a well-established brief, data science teams must follow best practices in executing the project. To help establish what that might mean, I have come up with ten points to provide a framework that can be applied to any data science project.
1. Understand the Problem
The most fundamental part of solving any problem is knowing exactly what problem you are solving. Make sure that you understand what you are trying to predict, any constraints, and what the ultimate purpose for this project will be. Ask questions early on and validate your understanding with peers, domain experts, and end-users. If you find that answers are aligning with your understanding, you know that you are on the right path.