# The Latest from the Maths & Science Section

This site has always had a strong flavour of both Mathematics and Science [1]; sometimes overt as in How to be Surprisingly Popular, Toast, Data Visualisation – A Scientific Treatment, Hurricanes and Data Visualisation: Part II – Map Reading or Patterns Patterns Everywhere; sometimes implicit as in Analogies or A Single Version of the Truth. These articles typically blend Business and Scientific or Mathematical ideas, seeking to find learnings from one applicable to the other. More recently, I have started writing some entirely Maths or Science-focused articles, starting with my book about Group Theory and Particle Physics, Glimpses of Symmetry [2]. These are collected in the Maths & Science Section, which can be accessed from the site menu; it’s right next to the Data & Analytics Dictionary as per:

Recent trends have begun to drive a confluence between Mathematics and aspects of the data arena, notably to do with Data Science and Artificial Intelligence approaches like Machine Learning [3]. For this reason, I will periodically share selected articles from the Maths & Science Section here on the main site. I hope that they are of interest to at least some regular readers. I’ll kick this off with three, interconnecting articles about one of the most important numbers in Mathematics, $e$, one of the most misunderstood, $\pi$, and finally their beautiful relationship to each other [4].

 1 Euler’s Number A long and winding road with the destination being, e, probably the most important number in Mathematics. 2 The Irrational Ratio The number π is surrounded by a fog of misunderstanding and even mysticism. This article seeks to address some common misconceptions about π, to show that in many ways it is just like any other number, but also to demonstrate some of its less common properties. 3 The Equation Deriving (and demystifying) one of the most famous equations in Mathematics.

I will occasionally share further content from the Maths & Science Section, particularly when there is some linkage to data matters or the other subjects that I cover in the main site.

Notes

 [1] No doubt related to its author’s background and indeed that of his wife. [2] Glimpses of Symmetry is about 75% finished at the time of writing. [3] A cornerstone of Machine Learning is of course Linear Algebra, a topic running throughout Glimpses of Symmetry from Chapter 6 onwards. [4] As may be obvious from elements of their content, these articles are presented out of chronological order, but the overall flow makes more sense this way round.

From: peterjamesthomas.com, home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases