Data Structure Training Course Content

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Data Structure TrainingCourse Content

What is data structure

In applied science, information|a knowledge|an information} structure could be a explicit method of organizing data in an exceedingly pc in order that it may be used expeditiously.
Data structures will implement one or additional explicit abstract information varieties (ADT), that specify the operations which will be performed on a knowledge structure and therefore the process complexness of these operations. compared, a knowledge structure could be a concrete implementation of the specification provided by associate degree ADT.[citation needed]Data Structure Training in hyderabad kukatpally
Different forms of information structures area unit suited to totally different forms of applications, and a few area unit extremely specialised to specific tasks. for instance, relative informationbases normally use B-tree indexes for data retrieval,while compiler implementations sometimes use hash tables to seem up identifiers.[citation needed]
Data structures offer a way to manage massive amounts of knowledge expeditiously for uses like massive databases and net categorization services. Usually, economical information structures area unit key to coming up with economical algorithms. Some formal style strategies and programming languages emphasize information structures, instead of algorithms, because the key organizing consider software system style. information structures may be accustomed organize the storage and retrieval of knowledge hold on in each main memory and secondary memory. for more info click here





Data Structure Course Content Details

01. Introduction

  •  What’s the Book About?
  •  Mathematics Review
  •  A Brief Introduction to Recursion
  • C++ Classes
  •  C++ Details
  •  Templates
  •  Using Matrices

02. Algorithm Analysis

  •  Mathematical Background
  •  Model
  •  What to Analyze
  •  Running Time Calculations

03.Lists, Stacks, and Queues

  • 3.1 Abstract Data Types (ADTs)
  • 3.2 The List ADT
  • 3.3 vector and list in the STL
  • 3.4 Implementation of vector
  • 3.5 Implementation of list
  • 3.6 The Stack ADT
  • 3.7 The Queue ADT

04.Trees

  • Preliminaries
  • Binary Trees
  • The Search Tree ADT–Binary Search Trees
  • AVL Trees
  • Splay Trees
  • Tree Traversals (Revisited)
  • B-Trees
  • Sets and Maps in the Standard Library

05.Hashing

  • General Idea
  • Hash Function
  • Separate Chaining
  • Hash Tables Without Linked Lists
  • Rehashing
  • Hash Tables in the Standard Library
  • Extendible Hashing




06. Priority Queues (Heaps)

  • Model
  • Simple Implementations
  • Binary Heap
  • Applications of Priority Queues
  • d-Heaps
  • Leftist Heaps
  • Skew Heap
  • Binomial Queues
  • Priority Queues in the Standard Library

07. Sorting

  • Preliminaries
  • Insertion Sort
  • A Lower Bound for Simple Sorting Algorithms
  • Shellsort
  • Heapsort
  • Mergesort
  • Quicksort
  • Indirect Sorting
  • A General Lower Bound for Sorting
  • Bucket Sort
  • External Sorting

08. Graph Algorithms

  • Definitions
  • Topological Sort
  • Shortest-Path Algorithms
  • Network Flow Problems
  • Minimum Spanning Tree
  • Applications of Depth-First Search
  • 9.7 Introduction to NP-Completeness

09 Algorithm Design Techniques

  • Greedy Algorithms
  • Divide and Conquer
  • Dynamic Programming
  • Randomized Algorithms
  • Backtracking Algorithms

10.Amortized Analysis

  • An Unrelated Puzzle
  • Binomial Queues
  • Skew Heap
  • Fibonacci Heaps
  • Splay Trees

11. Advanced Data Structures and Implementation

  • Top-Down Splay Trees
  • Red-Black Trees
  • Deterministic Skip Lists
  • AA trees
  • Treaps
  • k-d Trees
  • Pairing Heaps




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Updated: May 13, 2017 — 4:30 am

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