Hands-On Data Structures and Algorithms with Kotlin

Arrays, linked lists, queues, maps: knowing the names is one thing; understanding how they behave in code is another. Hands-On Data Structures and Algorithms with Kotlin builds that understanding step by step, connecting foundational computer science concepts with Kotlin implementations and the choices developers make when working with data.

Start with the way algorithms behave

The opening chapters introduce algorithms and data structures, then explain how to reason about efficiency through time and space complexity. That foundation gives readers a practical lens for comparing approaches—not just whether code works, but how its work and memory use change as the problem grows.

Build a toolkit of core data structures

From arrays and dynamic arrays to singly, doubly, and circular linked lists, the book examines how common structures hold and organize information. It continues with stacks, queues, circular queues, deques, and key-value maps, including hashing and collision handling. Implementation and operation topics help make the mechanics behind these structures more concrete.

Compare approaches to searching and sorting

Search topics range from linear and binary search to jump, exponential, and pattern-search methods, including Rabin–Karp and Knuth–Morris–Pratt. Sorting coverage includes bubble, selection, insertion, merge, quick, and heap sort. Seeing multiple strategies together helps readers consider which approach fits a particular collection or task.

Connect Kotlin collections with functional ideas

Later sections turn to Kotlin’s collection framework, including lists, sets, maps, and data operations. The book also introduces functional programming concepts such as immutability, pure functions, lambdas, higher-order functions, functional data structures, and typed functional programming.

A practical route through the subject

Chapter summaries and questions provide natural pauses to review the material. Kotlin developers and learners looking to strengthen their understanding of data structures, algorithm efficiency, and collection behavior will find a structured path from fundamentals toward more considered implementation choices.