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
7. Naive Bayes Classifier

Bonus Content

1. Expressing a Taylor Polynomial in Code

• More to come. New topics weekly 🤞