This is the first article in a series about the skills required to be a successful data scientist. One of the basic skills is a good foundational understanding of mathematics.
The mathematics field can be broken down into several sub-topics. The primary ones that concern a data scientist are calculus and linear algebra. A close third is the field of statistics. It is somewhat debatable if statistics is a branch of mathematics or an independent study. Statistics is a cornerstone to machine learning, therefore it deserves its own article, if not a series of articles. Additionally, probability can (and should) be studied independently of statistics, but it is so closely aligned that it only seems right to keep these subjects paired for now.
Calculus is is the mathematical study of continuous change, and provides equations and reasoning to explain it in a practical way. It can be thought of as the study of change in the same way that geometry is the study of shape and algebra is the study of generalizations of arithmetic operations. The origins of calculus are complex but the invention of the modern version of it is generally credited to Isaac Newton and Gottfried Leibniz with substantial foundations being laid prior to their time by intellectual giants.
Calculus is an important part of data science, because it helps us to analyze and…
