Learning PathLearning Functional Data Structure & Algorithm
Last updated 9/2017
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 636.12 MB | Duration: 5h 28m
Unlock complex data structures and algorithms and get the most out of functional programming
What you'll learn
Learn about the O notation
Get familiar with runtime and space complexities
Learn to drop, concatenate, append, and prepend lists
Understand what backtracking algorithm is
Learn to build cost-efficient applications
Learn how ADTs are implemented in a functional setting
Understand functional features such as lazy evaluation and recursion
Get to know Scala's best practices and idioms
Requirements
Working knowledge of functional language especially Scala is required.
Description
Data structures and algorithms are the fundamentals of computer programming. Functional data structures have the power to improve the code base of an application and improve 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.
So if you've got some prior knowledge on functional languages and wish to get acquainted with data structures and algorithms with simple functional implementation, then go for this Learning Path.
Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
Let's take a quick look at your learning journey. This Learning Path begins with an introduction to functional programming. You will learn about Lists, the work horse data type for most functional languages. You will then understand structural sharing and how it helps to make immutable data structures efficient and practical. Next, you will learn about binary trees wherein you will build complete trees, greedy algorithms, and backtracking. Moving ahead, you will seehow to use abstract data types (ADTs) such as stacks, queues, trees, and graphs and will learn to implement them in functional programming. Finally, you will learn implementation techniques such as amortization and lazy evaluation to ensure code efficiency.
By the end of this Learning Path, you will be able to write efficient functional data structures and algorithms for your applications.
About the Author
For this course, we have combined the best works of this esteemed author
Atul S. Khot is 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.
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 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.
Overview
Section 1: Functional Data Structures and Algorithms
Lecture 1 The Course Overview
Lecture 2 The Imperative Way and a Higher Level of Abstraction
Lecture 3 Functional Programming and Boilerplate
Lecture 4 Higher Order Functions
Lecture 5 Recursion Aids Immutability
Lecture 6 Copy-On-Write, Laziness, and Deferred Execution
Lecture 7 Composing Functions
Lecture 8 The Big O Notation
Lecture 9 Space/Time Trade-Off
Lecture 10 Referential Transparency
Lecture 11 Vectors Versus Lists
Lecture 12 Complexities and Collections
Lecture 13 First Steps
Lecture 14 List Head and Tail
Lecture 15 Drop Elements
Lecture 16 Concatenating Lists
Lecture 17 Persistent Data Structures and Tail Call Optimization
Lecture 18 List Append and Prepend
Lecture 19 Getting the Value at Index
Lecture 20 Modifying a List Value
Lecture 21 Node Definitions
Lecture 22 Building the Tree
Lecture 23 Comparing Trees
Lecture 24 The Accumulator Idiom
Lecture 25 Binary Search Trees
Lecture 26 Binary Numbers
Lecture 27 Greedy Algorithms and Backtracking
Section 2: Advanced Functional Data Structures and Algorithms
Lecture 28 The Course overview
Lecture 29 Reversing a List
Lecture 30 Exploring More About Graph Algorithms
Lecture 31 Cycle Detection
Lecture 32 Incrementing a Binary Number
Lecture 33 List of Tree Roots
Lecture 34 FIFO Queues
Lecture 35 Invariants
Lecture 36 Priority Queue
Lecture 37 Leftist Trees
Lecture 38 Functional Heaps
Lecture 39 Program Evaluation
Lecture 40 Argument Evaluation
Lecture 41 Memoization – Remembering Past Results
Lecture 42 Streams
Lecture 43 Some Algorithms on Stream
Lecture 44 Imperative Implementations and Amortization
Lecture 45 Strict Versus Lazy
Lecture 46 Streams
Lecture 47 Amortized Deques
Lecture 48 Concept of Red-Black Trees
Lecture 49 Working with Red-Black Trees
Lecture 50 Verifying the Transformation
Lecture 51 Binomial Trees
Lecture 52 A Binomial Heap
Lecture 53 Binary Number Equivalence
Lecture 54 Stable and Unstable Sorting
Lecture 55 Bubble Sort
Lecture 56 Selection Sort
Lecture 57 Insertion Sort
Lecture 58 Merge Sort
Lecture 59 Quick Sort
This Learning Path is for developers who have some experience in functional programming language,especially Scala.
Homepage
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