Data vs. Model: Which is More Important?
The two opponents walk into the ring, each claims to have the upper hand. The data scientist pulls out a silver ruler, the deep learning developer pulls out a gleaming hammer — who will build the best model?
In my previous positions, I’ve worked as both a data scientist and a deep learning algorithm developer. The differences between the two are not clear-cut.
Both deal with data and machine learning models, using similar success metrics and working principles.
So what makes them different?
I think it’s the attitude.
In my experience, deep learning developers (especially junior ones) tend to focus more on the model, while data scientists analyze and manipulate the data to make almost any model work.
Or, should I simplify it even further and say that:
Deep Learning = Model Oriented