Advanced Functional Data Structures and Algorithms

Algorithms and datastructures are fundamentals in computer programming. Functional data structures have the power to improve the codebase of an application and improve its efficiency. With the advent of functional programming and powerful functional languages such as Scala, Clojure, and Elixir becoming part of important enterprise applications, functional data structures have gained an important place in the developer toolkit.

Immutability is a cornerstone of functional programming. Immutable and persistent data structures are thread-safe by definition and therefore are very appealing to write robust concurrent programs. But how do we express traditional algorithms in a functional setting? Won’t we end up copying too much? Do we trade performance for versioned data structures? This course attempts to answer these questions by looking at functional implementations of traditional algorithms.

The course begins by showing you the functioning of lists, the workhorse data type for most functional languages. We’ll show you what structural sharing means and how it helps to make immutable data structures efficient and practical.

While writing code, we use ADTs (abstract data types) such as Stacks, Queues, Trees, and Graphs. You’ll see how these ADTs are implemented in a functional setting. We look at implementation techniques such as amortization and lazy evaluation to ensure efficiency. By the end of the course, you’ll be able to write efficient functional data structures and algorithms for your applications.

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