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

Learn how and when to use the right data structures in any situation, strengthening your computational thinking, problem-solving, and programming skills in the process.

This accessible and entertaining book provides an in-depth introduction to computational thinking through the lens of data structures — a critical component in any programming endeavor. You’ll learn how to work with more than 15 key data structures, from stacks, queues, and caches to bloom filters, skip lists, and graphs. You’ll also master linked lists by virtually standing in line at a cafe, hash tables by cataloging the history of the summer Olympics, and Quadtrees by neatly organizing your kitchen cabinets, all while becoming familiar with basic computer science concepts, like recursion and running time analysis.

Jeremy Kubica is an engineer director at Google, where he manages an applied artificial intelligence, machine learning, and data science team within the Cloud AI organization. The team focuses on applying state-of-the-art AI and ML to solve real-world problems. Before joining Cloud AI, Kubica led the adsquality efforts in the Pittsburgh office, including online quality predictions, advertiser policy checks, distributed machine learning infrastructure, and statistical analysis. He is a graduate of the Ph.D. program at Carnegie Mellon University's Robotics Institute. His thesis research focused on large-scale data mining problems and the search for structure in large, noisy data sets. Previously he received a B.S. in Computer Science from Cornell University and a M.S. in Robotics from Carnegie Mellon University. He is the author of multiple books designed to introduce people to computer science, including Computational Fairy Tales and The CS Detective.

Introduction
Chapter 1: Information in Memory
Chapter 2: Binary Search
Chapter 3: Dynamic Data Structures
Chapter 4: Stacks and Queues
Chapter 5: Binary Search Trees
Chapter 6: Tries and Adapting Data Structures
Chapter 7: Priority Queues and Heaps
Chapter 8: Grids
Chapter 9: Spatial Trees
Chapter 10: Hash Tables
Chapter 11: Caches
Chapter 12: B-Trees
Chapter 13: Bloom Filters
Chapter 14: Skip Lists
Chapter 15: Graphs
Conclusion