There are two main type of assumptions in a data science project:
- Your own assumption that you build up during your project.
- Other assumption that are already there when you began your project.
The first kind isn't too problematic. Most data scientist will naturally document why they did this or that in their project. Whether in their analysis or in a report, it's just natural.
However, the second type of assumption are often completely overlooked or difficult to distinguish from basic facts. This is one of the reason why it is so crucial to ask a heck load of question before even starting any project.
Even statement about the usefulness of the project or the expect impact should be documented and fact checked.
Having this proper mapping in place will unlock two things:
- Allow you to control the project better and to steer it in the right direction.
- Allow you to raise flags fast if an assumption is proven incorrect throughout an analysis.
This will instantaneously make you a much more effective data scientist! ✅