Here are some notes for data science classes that I have been teaching at Scranton:

Data Science Notes: I have collaborated with Prof. Jason Graham and librarian Kelly Banyas through a PA GOAL Grant to produce open source educational materials related to statistics and data science. Part of this project was the creation of open access data science notes. You can find the notes here:

Data Science Notes

These notes were originally designed for Scranton’s DS 201 class Introduction to Data Science, but anyone who’s interested in an introductory data science course should (hopefully) find them helpful for getting acquainted with the nuts and bolts of basic programming in R, the ggplot2 and dplyr packages, intro stat and probability, and a little bit on regression and machine learning.

Discrete Probability: Prof. Steven Dougherty (also at Scranton) runs a repository of discrete math notes for a variety of subjects related to discrete mathematics. Take a look at the notes here:

Discrete math resources

You might save yourself the cost of a textbook!

My contribution is a chapter on notes for discrete probability. You can find my notes here:

Discrete probability notes

These notes (along with Prof Graham’s notes on matrices and Prof. Murong Xu‘s notes on graph theory) were used for Scranton’s DS 210 class Mathematical Methods in Data Science. This class was meant as a crash course introduction to the basic concepts from probability and statistics found in data science. As mentioned in the introduction of the notes, it would be a mistake to consider these notes as a sufficient replacement for a probability class (it doesn’t cover continuous probability). Instead, they should be seen as a springboard for several concepts in applied probability.