Course Link: Combinatorics and Probability (part of the discrete math specialization)

I’m continuing to review and practice math concepts to be a better software engineer. Here are some thoughts if you want to go through the course and a list of external resources I’ve found helpful.

Although the course covers quite a few concepts and is well-structured, the prerequisites aren’t clarified in the description. I also had to supplement my study with other resources since this course doesn’t have everything I wanted to review.

I mainly complemented this course with the Statistics 110 course by Professor Joe Blitzstein. I’ve also went through various articles, videos, and tools to help develop a better intuition.

## Prerequisites Clarification

**Required**

- Logical Statements
- Sets (notation, relationships, operations, Venn diagrams)
- Python (loops, conditionals, functions, itertools)
- Required for the final assignment
- Knowing itertools is optional but it’ll make writing simulations easier

**Optional**

- Functions
- Calculus (if you want to derive some of the distributions)
- Series (Geometric, Taylor)

Read the Introduction and Preliminaries section (chapter 0) of this book.

Math Review Handout (Stat 110).

Translating Between Probability and Sets (Stat 110).

## Suggested Resources

There’s a separate section on Bayes’ Theorem because it was the most relevant topic for me (spent the most time on it).

### Book

### Complementary Course

- Statistics 110 by Professor Joe Blitzstein
- Lectures 1-10 recommended
- Watch the other lectures as required

### Bayes’ Theorem

These are ordered by suggested study order.

- Grant Sanderson’s (3Blue1Brown) video
- How Bayesian Inference Works
- Kalid Azad’s article
- Arbital’s Bayes’ Rule Guide

### Videos/Playlists/Channels

- A Student’s Guide to Bayesian Statistics (playlist)
- 3Blue1Brown
- JBStatistics
- Brandon Foltz
- Khan Academy
- How Selected Models and Methods Work

### Articles

- Linearity of Expectation
- An Introduction to Probability
- How Bayesian Inference Works
- Absolute Fundamentals of Machine Learning