This repository contains lecture notes for Columbia University's Applied Data Science course taught in the spring of 2013. The lecture notes are in LaTeX/TeX.
- This is not a book but lecture notes, i.e. it is inherently incomplete and has never been edited.
- There are likely typos and no guarantee that code samples will run in modern systems or versions.
- These notes primarily form a collection of instructions on how to do certain things. In particular, they represent the basic tool-set of what in 2013 could be described as a "data scientist," and some background on those tools.
- If you want to get up and running hacking with statistical models they provide one starting point.
- They are not explanations of why those things work, why it makes sense to do them and when, or what computing and machines learning is all about. They are akin to a technical music lesson which walks you the basics of playing Golberg's Variations with no mention of music, what that particular piece of music conveys, or of Bach himself.
- Finally, these notes represent the authors' views in 2013, very early in their carreer, which do not necessarily transfer to the present day.
These notes are released under the Creative Commons Zero v1.0 Universal License, which means you are free to use these notes, and the code to generate them, as you see fit. But we ask you to cite the source as you do so.