Deep Learning vs Data Science: Who Will Win? | by Benjamin Bodner

What is more important, your data or your model?

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. If you ask me what the differences are between the two, I’ve got to say that it’s not clear-cut.

Both deal with data and machine learning models, and both use similar success metrics and working principles.

So what makes them different?

I think its the attitude.

I’ll be bold and generalize that from my experience, deep learning developers (especially junior ones) tend to focus more on the model, while data scientists do the opposite — they analyze and manipulate the data such that almost any model will do the trick.

Or, should I dare to simplify it even further and say that:

Deep Learning = Model Oriented

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