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This book grew out of a simple observation: most software engineers use algorithms and data structures every day, yet many feel uncertain about the fundamentals. They may use a hash map or call a sorting function without fully understanding the guarantees those abstractions provide, or they may struggle when a problem requires designing a new algorithm from scratch. At the same time, Computer Science students often encounter algorithms in a highly theoretical setting that can feel disconnected from the code they write in practice.
Algorithms with TypeScript bridges that gap. It presents the core algorithms and data structures from a typical undergraduate algorithms curriculum --- roughly equivalent to MIT's 6.006 and 6.046 --- but uses TypeScript as the language of expression. Every algorithm discussed in the text is implemented, tested, and available in the accompanying repository. The implementations are not pseudocode translated into TypeScript; they are idiomatic, type-safe, and tested with a modern toolchain.
This book is written for two audiences:
Software engineers who want to solidify their understanding of algorithms and data structures. Perhaps you learned this material years ago and want a refresher, or perhaps you are self-taught and want to fill in the gaps. Either way, seeing algorithms in a language you likely use at work --- TypeScript --- makes the material immediately applicable.
Computer Science students who are taking (or preparing to take) an algorithms course. The book follows a standard curricular sequence and includes exercises at the end of every chapter. The TypeScript implementations let you run, modify, and experiment with every algorithm.
The book assumes you can read and write basic TypeScript or JavaScript. You should be comfortable with functions, loops, conditionals, arrays, and objects. No prior knowledge of algorithms or data structures is required --- we build everything from the ground up, starting with the definition of an algorithm in Chapter 1.
Some chapters use mathematical notation, particularly for complexity analysis. Chapter 2 introduces asymptotic notation (O, Ω, Θ), and the Notation section that follows this preface summarizes all conventions used in the book. A comfort with basic algebra and mathematical reasoning is helpful but not strictly required; we explain each concept as it arises.
The book is organized into six parts:
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