3.5 hours







Packt Publishing

Get acquainted with complex data structures and algorithms with simple functional implementations

Expected learning & outcomes

  • Understand how to think in the functional paradigm
  • Build cost-efficient applications
  • Explore important algorithms for ordering dependencies
  • See common data structures and the associated algorithms, and the context they are commonly used in
  • See how ADTs are implemented in a functional setting
  • Explore the basic theme of immutability and persistent data structures
  • Find out how the internal algorithms are redesigned to exploit structural sharing, so that the persistent data structures perform well, avoiding needless copying
  • Understand functional features such as lazy evaluation and recursion used to implement efficient algorithms
  • Get to know Scala’s best practices and idioms

    Skills you will learn

    Actuary, Algorithms, Big Data, Book Writing, C# programming, C++ programming, Chartered accounting, Consulting, Data Science, Development, Hadoop, Java, Linux, Mathematics, Oracle

    About this course

    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.

    About the authors

    Atul S. Khot grew up in Marathwada, a region of the state of Maharashtra, India. A self-taught programmer, he started writing software in C and C++. A Linux aficionado and a command-line guy at heart, Atul has always been a polyglot programmer. Having extensively programmed in Java and dabbled in multiple languages, these days he is getting increasingly hooked on Scala, Clojure, and Erlang. Atul is a frequent speaker at software conferences, and a past Dr. Dobb's product award judge. In his spare time, he loves to read classic British detective fiction. He is a foodie at heart and a pretty good cook. Atul someday dreams of working as a master chef, serving people with lip-smacking dishes.

    He was the author of Scala Functional Programming Patterns published by Packt Publishing in December 2015. The book looks at traditional object-oriented design patterns and shows people how they can use Scala's functional features instead.

    Raju Kumar Mishra is a consultant and corporate trainer for big data and programming. After completing his B. Tech from the Indian Institute of Technology (ISM) Dhanbad, he worked for Tata Steel. His deep passion for mathematics, data science, and programming took him to the Institute of Science (IISc). After graduating from IISc in computational science, he worked for Oracle as a performance engineer and software developer.

    He is an Oracle-certified associate for Java 7. He is a Hortonworks-certified Apache Hadoop Java developer, and holds a Developer Certification for Apache Spark (O'Reilly School of Technology and Databriks), and Revolution R Enterprise-certified Specialist Certifications. As well as this, he has also passed the Financial Risk Manager (FRM I) exam. His interest in mathematics helped him in clearing the CT3 (Actuarial Science) exam.


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