So just where does machine learning fit in to the scientific world? And are we eventually going to have to resort to such methods to make progress in modelling if mathematical models cannot keep up? I'd like to argue that machine learning and mathematical models can coexist nicely in both research and industrial applications, and that the most interesting models appear when we utilise the power of both paradigms. To illustrate this point we take a trip to a civilisation who are in the contradictory position of having invented machine learning (implemented in PyTorch, strangely...) but their mathematics is not up to scratch. In particular they are not familiar with modern calculus. This example should hopefully replicate and illuminate the situation we find ourselves in now - where mathematics is difficult but data collection is easy.

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I've always been a fan of maps, especially old maps, and the sense of exploration and history that comes with hovering over them and zooming in on the fine details. Despite this I've never really tried to make any maps of my own. I recently discovered the nifty Python package, Folium, which can make visually stunning interactive maps within a matter of minutes. Since my PhD research is concerned more with the digital world than the real world I decided to look to my love of cycling and plot some of the top cycling climbs in the UK.

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Over the course of my studies I've worked on multiple different machines, with different operating systems, different hardware, and different security privileges. It can be a nightmare to ensure a smooth and consistent experience, but when done right, makes working with code and data a pleasure and makes academic reproducibility that much easier. In this article we will look at how the right choice of Python distribution, environment, and IDE can go along way to avoiding technological headaches.

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