How Practical Is Python For Prototyping Data Science Projects At Scale?

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Originally published on Towards AI.

The Naive Solution

In this article, I will share my thoughts on the practicality of using Python to work on compute-intensive data science projects. The primary audience for this article is data scientists working in the industry and, to a lesser extent, researchers who rely on computational methods to answer their research questions.

The code to reproduce the results described in this article can be found in this repo.

In most cases that arise in an academic environment, it therefore makes sense to develop in ordinary Python, identify computational bottlenecks, and use Numba to remove them.

The quote above is from the abstract of [1]. For… Read the full blog for free on Medium.

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