Free Download A Programmer’s Guide to Computer Science (A virtual degree for the self-taught developer) Vol.1 Book in PDF written by William M. Springer II, PhD and published by Jaxson Media, Madison, WI, United States of America.
According to the Author, “The developers I know come from a wide variety of backgrounds. Some have graduate degrees in computer science; others studied photography, math, or don’t even have a college degree. Over the last few years I’ve noticed more of a desire from working programmers to learn computer science for a number of reasons. While many people will find the topics covered here interesting in their own right, I’ve attempted to also show where this knowledge is useful in real-world (non-academic) situations. The goal is that when you’ve finished this book, you will know the basics of what you would have learned from an undergraduate computer science degree and how to apply it.
Simply put, this book aims to make you a better, more knowledgeable programmer through a greater understanding of computer science. I can’t consolidate two decades of college and professional experience into one book… but I can try to hit the good parts. My hope is that you will find at least one concept where you can say “yeah, that makes sense now” and apply it to your everyday work.
The point of this book is to make the reader more comfortable with computer science concepts and how to apply them, rather than to fully replicate four years of study. In particular, this is not a book on proofs. While part VIII in Volume II covers proof techniques, standard algorithms are generally given without proofs of correctness. The idea is that the reader will emerge knowing of the existence of these algorithms and how to use them, without getting bogged down in the fine details. For a book of proofs, written at the graduate level, I highly recommend Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein, generally referred to as CLRS. More in-depth reading material is often referenced in the footnotes as well.
This is also not a book on programming; you won’t find a discussion of when to use integers vs doubles or an explanation of loops. Indeed, it is expected that the reader will be able to understand pseudocode listings used to describe algorithms1. The intent is to tie computer science concepts to programming practices that are already familiar to the reader.
Table of Contents
- Asymptotic Runtime
- Data Structures
- Classes of problems
- Introduction to Graph Theory
- Data Structures on Graphs
- Well-Known Graph Algorithms
- Common Graph Classes
- Sorting Algorithms
- Brute Force or No
- Dynamic Programming
- Greedy Algorithms
- Understanding Complexity Theory
- Languages and State Machines
- Turing Machines
- Appendices
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