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 understand complex patterns in data. Data science incorporates a wide variety of tools and techniques, such as data analysis, machine learning, artificial intelligence, statistics, and data visualization. All of these techniques are underpinned by the mathematics of calculus. The techniques all rely on algorithms that are based on calculus.

The way to learn calculus, or refresh your memory, is to do it for free or for a fair price. Much like good journalism in the twenty-first century, if you want good skill tutorials you may have to pay for them in one way or another.

Linear Algebra is a branch of mathematics that deals with equations and systems of linear equations. A data scientist can use linear algebra to understand and analyze large datasets. Matrices and vectors act as tools to better explain the nature of your data set.

Discrete math is a collection of mathematical techniques that are particularly useful…