Data Structures and Algorithms with Go

Dive into the endless possibilities of data structures and algorithms and have fun doing it

Key Features
Become familiar with common data structures.
Learn and understand the most popular algorithms through practical examples.
Recognize when a particular data structure or algorithm should be used to create an efficient software solution.

Description
Go, designed by Google, is a modern, open-source language known for its simplicity, readability, and efficiency. It excels at building web applications, network tools, and cloud services. Its clear syntax and built-in concurrency features make it a popular choice for modern developers.

This guide simplifies the basics by introducing arrays, lists, stacks, queues, maps, trees, and graphs in a practical way. Get hands-on experience, understand essential operations, and compare strengths and weaknesses. Perfect your skills with searching, sorting, and efficient data retrieval techniques. Traverse graphs and trees with ease, all illustrated in the Go code for real-world application, and conclude with insights for ongoing learning.

After reading this book, the reader can determine when and why specific data structures should be used and when an algorithm best fits the actual problems solution.

What you will learn
Decide which data structure is the most suitable for a particular problem.
Implement different algorithms with the Go programming language.
Recognize which algorithm is best suited for certain scenarios.
Utilize data structures and algorithm implementations from Gos standard library.
Learn how real-life problems can be solved and simulated.

Who this book is for
The book targets beginners and experienced developers who want to learn how to implement particular algorithms. It is also helpful for developers who wish to expand their knowledge of data structures and algorithms.

Table of Contents
1. Fundamentals of Data Structures and Algorithms
2. Arrays and Algorithms for Searching and Sorting
3. Lists
4. Stack and Queue
5. Hashing and Maps
6. Trees and Traversal Algorithms
7. Graphs and Traversal Algorithms