Jeremy Kubica - Data Structures the Fun Way: An Amusing Adventure with Coffee-Filled Examples

This is a book about computational thinking through the lens of data structures, constructs for organizing and storing data. It is more than a cookbook of handy data structures. Rather, it explores the thinking behind these structures and their fundamental impact on solving complex problems, using real-world analogies to make abstract computational concepts intuitive. The goal of this book is to provide new insights into how you can use preexisting structure within the data to your advantage or create new structures to efficiently solve problems.

Among other things, I discuss the differences between arrays and linked lists, the complexity and power of pointers, the effect of data structures on algorithmic behavior, the branching of tree-based data structures, mathematical mappings in hash tables, and the usefulness of randomization. In short, youll learn to think about algorithms by investigating different ways to organize the data they process. Youll also apply these computational approaches to real-world problems, a surprising number of which focus on procuring a decent cup of coffee.

Understanding how data structures function is critical to using them effectively. Just as an experienced carpenter wouldnt pound screws into wood with a hammer or use sandpaper to cut a two-by-four in half, an experienced programmer needs to choose the right tools for every job. As well see repeatedly throughout the following chapters, every data structure comes with tradeoffs. Saws cut through wood more effectively than sandpaper but create coarse edges. There is no single data structure that is perfect for every possible use case, but this is what makes computer science and the development of algorithms so interesting. A good computer scientist must understand how different data structures behave in order to determine where they can be best used.

This book focuses on a few canonical data structures and uses them to explore fundamental themes in computational thinking. Each of these data structures is a useful exemplar of a more general class of data structures and of a conceptual approach. For example, B-trees demonstrate one approach to the problems of keeping search trees balanced and optimizing for expensive memory accesses. I discuss the tradeoffs between memory usage and accuracy with Bloom filters; the use of randomization with skip lists; and how to capture multidimensional structure with grids, quadtrees, or k-d trees. As such, this book is neither an introduction to programming, a comprehensive anthology of data structures, nor a full analysis of brewing coffee (although we will touch repeatedly on this important topic). Our goals are differentto develop mental tools that apply across a range of specific problems and programming languages.

This book is for anyone who wants to learn more about the thinking behind the data structures that lie at the heart of computer science. I assume such basic familiarity with programming as can be expected after taking an introductory course, participating in a boot camp, or working through a beginners programming book. Readers should be familiar with fundamental programming concepts such as variables, loops, and conditional statements. Some more adventurous readers might even have coded up some of the data structures or algorithms in this book already or might do so as they read through it. However, you wont need to know the specific details of particular programming languages or algorithms.