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Easy to Advanced Data Structures

A complete guide to learning everything there is to know about data structures

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    4.9       1235 students enrolled this month

Offer by ATTITUDE ACADEMY    Last updated:- Feb 20, 2024

What you'll learn

  • Mature understanding of data structures
  • Algorithms associated with data structures
  • Dynamic arrays
  • Singly and doubly linked list
  • Queues & Stacks
  • Binary Trees and Binary search trees
  • Heaps & Priority queues
  • Union find/Disjoint set
  • Hash table/Associative array
  • Fenwick tree/Binary indexed tree

Requirements

  • C  with function n pointer
  • C++ with function n pointer
  • Basic of computers

Description

Data structures are amongst the most fundamental ingredients in the recipe for creating efficient algorithms and good software design. Knowledge of how to create and design good data structures is an essential skill required in becoming an exemplary programmer. This course will teach you how to master the fundamental ideas surrounding data structures.

  • Importance of data structures
  • Common use cases for data structures
  • Big O notation and algorithm analysis
  • Key data structure terminology

 

  • Introduction to arrays and linked lists
  • Implementing arrays and linked lists
  • Operations and methods for arrays and linked lists
  • Performance analysis

  • Implementing stacks and queues
  • Use cases for stacks and queues
  • Array-based and linked list-based implementations
  • Solving problems using stacks and queues

  • Introduction to tree data structures
  • Binary trees and their properties
  • Tree traversal algorithms (in-order, pre-order, post-order)
  • Binary tree operations

  • Properties of BST
  • Searching, insertion, and deletion in BST
  • Balancing BST (e.g., AVL trees)
  • BST operations and their complexity

  • Introduction to heaps
  • Binary heaps and heap operations
  • Implementing priority queues
  • Heapsort and heap-based algorithms

  • Hash functions and collision resolution
  • Implementing hash tables
  • Analysis of hash table performance
  • Practical applications of hashing

  • Introduction to graph data structures
  • Representing graphs in various forms
  • Graph traversal algorithms (e.g., depth-first and breadth-first search)
  • Shortest path algorithms (e.g., Dijkstra's and Bellman-Ford)

  • Red-Black Trees
  • B-trees and B+-trees
  • Trie data structures
  • Graph-based data structures

  • Time and space complexity analysis
  • Common sorting algorithms (e.g., bubble sort, insertion sort, quicksort, merge sort)
  • Analysis of sorting algorithms and their efficiency
  • External sorting and multiway merge

  • Linear search and binary search
  • Hash-based searching
  • Search trees (e.g., B-trees for searching)
  • Advanced searching algorithms (e.g., A* search)

  • Introduction to dynamic programming
  • Memoization and tabulation
  • Solving problems using dynamic programming
  • Applications in real-world scenarios

  • Advanced data structures and their applications
  • Advanced algorithm design techniques
  • Solving complex problems
  • Introduction to computational complexity theory

  • Greedy algorithms and their applications
  • Divide and conquer algorithms
  • Randomized algorithms
  • Approximation algorithms

  • Students work on a data structure and algorithm project
  • Building a portfolio of data structure and algorithm projects
  • Project presentation and peer review

  • Review of key takeaways and skills acquired
  • Opportunities for further learning and career prospects in data structures and algorithms
  • Preparing for certifications and advanced topics

Limited Period Offer
INR 999/- INR 15000/- 93% Off
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Includes

What you'll learn

  • Mature understanding of data structures
  • Algorithms associated with data structures
  • Dynamic arrays
  • Singly and doubly linked list
  • Queues & Stacks
  • Binary Trees and Binary search trees
  • Heaps & Priority queues
  • Union find/Disjoint set
  • Hash table/Associative array
  • Fenwick tree/Binary indexed tree

Requirements

  • C  with function n pointer
  • C++ with function n pointer
  • Basic of computers

Description

Data structures are amongst the most fundamental ingredients in the recipe for creating efficient algorithms and good software design. Knowledge of how to create and design good data structures is an essential skill required in becoming an exemplary programmer. This course will teach you how to master the fundamental ideas surrounding data structures.

  • Importance of data structures
  • Common use cases for data structures
  • Big O notation and algorithm analysis
  • Key data structure terminology

 

  • Introduction to arrays and linked lists
  • Implementing arrays and linked lists
  • Operations and methods for arrays and linked lists
  • Performance analysis

  • Implementing stacks and queues
  • Use cases for stacks and queues
  • Array-based and linked list-based implementations
  • Solving problems using stacks and queues

  • Introduction to tree data structures
  • Binary trees and their properties
  • Tree traversal algorithms (in-order, pre-order, post-order)
  • Binary tree operations

  • Properties of BST
  • Searching, insertion, and deletion in BST
  • Balancing BST (e.g., AVL trees)
  • BST operations and their complexity

  • Introduction to heaps
  • Binary heaps and heap operations
  • Implementing priority queues
  • Heapsort and heap-based algorithms

  • Hash functions and collision resolution
  • Implementing hash tables
  • Analysis of hash table performance
  • Practical applications of hashing

  • Introduction to graph data structures
  • Representing graphs in various forms
  • Graph traversal algorithms (e.g., depth-first and breadth-first search)
  • Shortest path algorithms (e.g., Dijkstra's and Bellman-Ford)

  • Red-Black Trees
  • B-trees and B+-trees
  • Trie data structures
  • Graph-based data structures

  • Time and space complexity analysis
  • Common sorting algorithms (e.g., bubble sort, insertion sort, quicksort, merge sort)
  • Analysis of sorting algorithms and their efficiency
  • External sorting and multiway merge

  • Linear search and binary search
  • Hash-based searching
  • Search trees (e.g., B-trees for searching)
  • Advanced searching algorithms (e.g., A* search)

  • Introduction to dynamic programming
  • Memoization and tabulation
  • Solving problems using dynamic programming
  • Applications in real-world scenarios

  • Advanced data structures and their applications
  • Advanced algorithm design techniques
  • Solving complex problems
  • Introduction to computational complexity theory

  • Greedy algorithms and their applications
  • Divide and conquer algorithms
  • Randomized algorithms
  • Approximation algorithms

  • Students work on a data structure and algorithm project
  • Building a portfolio of data structure and algorithm projects
  • Project presentation and peer review

  • Review of key takeaways and skills acquired
  • Opportunities for further learning and career prospects in data structures and algorithms
  • Preparing for certifications and advanced topics

Placed Learners


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