Reading any scientific research paper can often be intimidating when you see large sections of mathematical notation – to the untrained eye at least, it can look like some foreign language that only intelligent people can understand. You’ll realise soon enough that the fundamental elements of these equations, broken down, are much simpler concepts than many of the complex ideas portrayed in the English language. Breaking the equations down and googling each element will let you build a picture of what that equation means. Soon, you’ll begin decoding and understanding what the equations means from understanding what each element means.

A good data scientist can take an equation, understand the variables involved, understand the operators, such as ‘sum over’ or ‘series product’, then turn that single or set of equations into pseudocode. The pseudocode forms the outline for an algorithmic implementation of the methodology described in the paper. In reality, the majority of the important research that’s implemented regularly, such as many of the machine learning algorithms have already been codified and incorporated into some package for you. However being able to understand the mathematics of these implementations is a crucial part of the job of a data scientist. I would suggest having a mathematical notation ‘cheat sheet’ to hand, which you can either write yourself or find online. Use it like you would use a dictionary, translate the mathematical notation into concepts that you understand in your head.