Many programmers often express an interest in improving their math skills. When probed on why they haven't acted on it, a common trend I found was due to how difficult it can be to build intuition around mathematical concepts.

In my journey of self-improvement and growth, I discovered that conceptualizing mathematical problems as code made them more intuitive. Expressing problems as computations provided a pathway to interact with concepts more practically when compared to traditional paper-and-pencil approaches many of us were taught in school.

This series aims to be break down Probability and Statistics in a way that engineers at any level of mathematical proficiency can understand. It's a great starting point with a lot of practical application in engineering.

This is as much a learning journey for me as it is for you. I hope you find it as useful as I do.

Table of Contents

1. Frequency Distributions
2. Measures of Central Tendency
3. Measures of Dispersion
4. Introduction to Probability
5. Conditional Probability
6. Bayes Theorem

Bonus Content

1. Expressing a Taylor Polynomial in Code

• More to come. New topics weekly 🤞