Introduction

When it comes to Data Science in 2020, there are a lot of options to choose from to do nearly anything. This is both a very positive thing, because there are more tools available for scientists to work and predict with, and a negative thing. This is because with so many options it can be incredibly difficult to articulate what particular tool might adhere to a situation or be optimal towards solving a problem.

One newer piece of scientific technology that I accidentally introduced myself to while doing research is the Julia programming language. At the time, I had been writing models in Python with C-based back-ends, along with some Sklearn and Tensorflow models which I found maintaining to be rather complicated. For my specific scenario, I found myself running into Pythonic timeouts all of the time while moving massive heaps of data via requests.

The C back-end has certainly proven itself to be quite venerable in scenarios like this, but also present a problem. Most Python modules that work to compete in speed with other solutions in more imperative languages are primarily written in C, not Python. For me this made the whole system quite a bit more irritating, developing software and carrying it over to work from a high-level Python front-end.

To be clear, it is certainly a sensible solution, C is probably one of the best ways something can quickly be reiterated on and be reliable, so it makes sense that the fastest, industry standard tools would follow this system. However, it is still important to remember that C is a different language, despite Python’s interpretation by C; meaning that proprietary implementations of C will often need some sort of dependency system.

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