Master in Data Structure at top institutes in Uttam Nagar & Yamuna Vihar

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

What placement assistance will you receive?

Free Placement Preparation Training

Access to curated Internships & Current Job Openings.

Top performers will be highlighted on Attitude Job portal

Requirements

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.

Course Circullum

Introduction to Data Structures

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

Arrays and Linked Lists

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

Stacks and Queues

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

Trees and Binary Trees

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

Binary Search Trees (BST)

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

Heaps and Priority Queues

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

Hashing and Hash Tables

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

Graphs and Graph Algorithms

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)

Advanced Data Structures

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

Algorithm Analysis and Sorting

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

Searching Algorithms

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

Dynamic Programming

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

Advanced Topics

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

Algorithm Design and Analysis

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

Final Project and Portfolio Building

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

Course Conclusion and Next Steps

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

Take the final exam online to complete the Easy to Advanced Data Structures after which you will be able to download your certificate from Attitude Trainings.