Here's the first post of a few planned within a fermentation series. One of the key ingredients to any drink, whether beer, kombucha, hop water etc is of course water, and not all waters are created equal. If water is the base of a drink, some flavours are better complimenteted by a blank canvas, while minerality can be used to make certain flavours pop. A common analogy is when cooking, flavours are brought out using salt, vinegar and other condiments. Getting this balance right can make all difference. Conversely if you're starting point is too rich in these properties, it's probably worth considering diluting, or even switching out for low-minerality bottled water. A common recommendation is to look in to the properties of your water at home, investigate what the properties of your water are, and see where you can go from there to tweak your water profile using common additives such as gypsum, baking soda and salt.

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Probably the main reason I started studying Geology was for the opportunities that it gives you to travel. In the first instance, I thought spending two weeks in the Lake District would be a good break while studying maths and computing at college. It turned out this break would end up being 17 years, although as lovely as they are, thankfully that entire time wasn't spent in the lakes. Needless to say, I love looking at and making maps, so exploring georeferenced data is a natural progression as I'm learning about data science.

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One of the themes for this term is algorithm optimisation, so I've been looking over industry standard sort and search algorithms, ever trying to minimise the 'Big O'. A classmate shared these sort visualisations by morolin, which really help to visualise the differences in efficiency between the algorithms. While these visualisations were created in Golang, they have since been translated in to Python by Kirk Kaiser.

Let's face it, writing code to sort a list isn't particularly interesting, so I wanted to see if I could impliment and visualise my own sort algorithm(s). The next step of course, was to see if similar visualisations could be created to demonstrate search algorithms..

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