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  • Part Time
  • Duration           :      2 Months
  • Class                :      1.30 Hrs Daily
  • Days                 :      Mon-Fri.
  • Half Time
  • Duration           :      6 Weeks
  • Class                :      3.00 Hrs Daily
  • Days                 :      Mon-Fri.
  • Full Time
  • Duration           :      3 Weeks
  • Class                :      4.30 Hrs Daily
  • Days                 :      Mon-Fri.

Course Description:

A good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures.

 

INTRODUCTION

  • Algorithms
  • Rate of Growth,
  • Types of Analysis
  • Asymptotic Notation
  • Big-O Notation
  • Master theorem for Divide and Conquer
  • Method of Guessing and Confirmation

RECURSION AND BACKTRACKING

  • Recursion
  •  Format of Recursive Function
  •  Recursion vs Iteration
  • Algorithms of Recursion
  • Backtracking
  • Algorithms of Backtracking

LINKED LISTS

  • Linked List ADT
  • Comparison of Linked Lists with Arrays & Dynamic Arrays
  • Singly Linked Lists
  • Doubly Linked Lists
  • Circular Linked Lists

STACKS

  • Implementation using Array
  • Implementation using Linked List

QUEUES

  • Implementation using Array
  • Implementation using Linked List

TREES

  • What is a Tree? Binary Trees
  • Generic Trees (N-ary Trees)
  • Threaded Binary Tree
  • Traversals (Stack or Queue-less Traversals)
  • Binary Search Trees (BSTs)
  •  Balanced Binary Search Trees
  • AVL (Adelson-Velskii and Landis) Trees

PRIORITY QUEUES AND HEAPS

  • What is a Priority Queue? Priority Queue ADT
  •  Priority Queue Applications
  • Priority Queue Implementations
  • Heaps and Binary Heaps

DISJOINT SETS ADT

  • Introduction
  •  Equivalence Relations and Equivalence Classes
  •  Disjoint Sets ADT Applications
  •  Tradeoffs in Implementing Disjoint Sets ADT
  • Fast UNION  Implementation (Slow FIND), Fast UNION Implementations  (Quick FIND)
  • Path Compression
  • Summary

GRAPH ALGORITHMS

  • Introduction
  • Applications of Graphs
  • Graph Representation
  • Graph Traversals
  • Topological Sort
  • Shortest Path Algorithm
  • Minimal Spanning Tree

SORTING

  • Classification of Sorting Algorithms
  •  Bubble Sort, Selection Sort
  • Insertion Sort
  • Shell Sort
  •  Merge Sort
  • Heap Sort
  • Quick Sort
  • Tree Sort
  • Comparison of Sorting Algorithms
  • Linear Sorting Algorithms, Counting Sort
  •  Bucket Sort (or Bin Sort), Radix Sort
  • Topological Sort
  • External Sorting

SEARCHING

  • Types of Searching
  • Unordered Linear Search
  •  Sorted/Ordered Linear Search
  •  Binary Search
  • Interpolation Search
  • Comparing Basic Searching Algorithms
  • Symbol Tables and Hashing
  • String Searching Algorithms

SELECTION ALGORITHMS [MEDIAN]

  • What are Selection Algorithms?
  • Selection by Sorting
  • Partition-based Selection Algorithm
  • Linear Selection Algorithm - Median of Medians Algorithm
  • Finding the K Smallest
  • Elements in Sorted Order

HASHING

  • Introduction
  • HashTable ADT
  • Understanding Hashing
  • Components of Hashing
  • Hash Table
  • Hash Function
  •  Load Factor
  • Collision
  •  Collision Resolution
  • Techniques
  • Separate Chaining
  • Open Addressing
  • Comparison of Collision Resolution Techniques, How Hashing Gets O(1)
  • Complexity? Hashing Techniques, Problems for which Hash Tables are not suitable
  • Bloom Filters

STRING ALGORITHMS

  • String Matching Algorithms
  • Brute Force Method
  • Rabin-Karp String Matching Algorithm
  •  String Matching with Finite Automata
  • KMP Algorithm
  • Boyer-Moore Algorithm
  •  Data Structures for Storing Strings
  • Hash Tables for Strings, Binary Search Trees for Strings
  • Tries
  • Ternary Search Trees
  • Comparing BSTs
  • Tries and TSTs

ALGORITHMS DESIGN TECHNIQUE

  • Introduction
  • Classification
  •  Classification by Implementation Method
  •  Classification by Design Method
  •  Other Classifications

GREEDY ALGORITHMS

  • Introduction
  • Greedy Strategy
  •  Elements of Greedy Algorithms
  •  Does Greedy Always Work? Advantages and Disadvantages of Greedy Method
  •  Greedy Applications
  • Understanding Greedy Technique

DIVIDE AND CONQUER ALGORITHMS

  • Introduction
  • What is Divide and Conquer Strategy? Does Divide and Conquer
  • Always Work? Divide and Conquer Visualization
  •  Understanding Divide and
  • Conquer
  •  Advantages of Divide and Conquer
  •  Disadvantages of Divide and Conquer
  • Master Theorem
  • Divide and Conquer Applications

DYNAMIC PROGRAMMING

  • Introduction
  • What is Dynamic Programming Strategy? Properties of Dynamic
  • Programming Strategy, Can Dynamic Programming Solve All Problems?
  • Dynamic
  • Programming Approaches, Examples of Dynamic Programming Algorithms
  • Understanding Dynamic Programming

Data Structures and Algorithm Training center in West Delhi, Uttam nagar. Best certified computer course curriculum for professional learning institute near Dwarka, Uttam Nagar, Jankapuri, Tilak Nagar, Subhash Nagar, Tagore Garden, Rajouri Garden, Ramesh Nagar, Moti Nagar, Kirti Nagar, Shadipur, Patel Nagar, Rajendra Place, Karol Bagh, Jhandewalan, Ramakrishna Ashram, Connaught Place, vikaspuri, najafgarh, Delhi Cantt, Dhaula Kuan, Palam Colony, Palam Villag, Dabri, Hari Nagar, Fateh Nagar, Raja Garden, Punjabi Bagh, Shivaji Park, Paschim Vihar, Peeragarhi, Inderlok, Netaji Subhash Place, Pitampura, Rohini, Chandni Chowk, New Delhi, East Delhi, North Delhi, West Delhi.



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