Advance Data Structure

Course Overview

Master Data Structures and Algorithms with hands-on implementation in C, C++, Java, Python, and JavaScript. Learn problem-solving patterns used in top coding interviews and competitive programming.

Duration: 24 weeks
Rating: 4.8 / 5
3,100+ Students

Detailed Syllabus

1: Programming Foundations & OOP

  • Introduction to Programming Logic and Syntax
  • Data Types, Variables, and Operators
  • Control Statements and Loops
  • Functions and Recursion
  • Arrays and Strings Basics
  • Pointers and Memory Management (C/C++)
  • Introduction to Object-Oriented Programming
  • Classes, Objects, Constructors, and Inheritance

Tools: VS Code, GCC, Code::Blocks, OnlineGDB

2: Core Data Structures – Linear

  • Arrays – Static & Dynamic Memory
  • Linked Lists – Singly, Doubly, and Circular
  • Stacks – Implementation using Arrays & Linked Lists
  • Queues – Simple, Circular, and Priority Queues
  • Deques and Custom Implementations
  • String Manipulation & Pattern Matching
  • Complexity Analysis – Time and Space

Tools: LeetCode, HackerRank, GeeksforGeeks

3: Non-Linear Data Structures

  • Introduction to Trees and Binary Trees
  • Binary Search Trees (BST)
  • Tree Traversal – Inorder, Preorder, Postorder
  • AVL Trees, Red-Black Trees, and Heaps
  • Priority Queues using Heap
  • Graphs – Representation (Adjacency List & Matrix)
  • Graph Traversal – BFS, DFS
  • Topological Sorting and Cycle Detection

Tools: Visualgo, Codeforces, LeetCode

4: Hashing & Advanced DSA

  • Hash Tables – Implementation and Collisions
  • Chaining and Open Addressing
  • Tries – Prefix Trees
  • Union-Find (Disjoint Set Union)
  • Segment Trees and Fenwick Trees (BIT)
  • Matrix-based Problems and Search Algorithms
  • Practical DSA Design Patterns

Tools: HackerEarth, InterviewBit

5: Algorithms & Problem Solving

  • Sorting Algorithms – Quick, Merge, Heap, Counting, Radix
  • Searching Algorithms – Binary, Linear, Exponential
  • Divide & Conquer Paradigm
  • Dynamic Programming Fundamentals
  • Memoization vs Tabulation
  • Greedy Algorithms and Optimization Problems
  • Backtracking (N-Queens, Sudoku, Subset Sum)
  • Bit Manipulation Techniques

Tools: LeetCode, CodeChef, AtCoder

6: Competitive Programming & Interview Prep

  • Algorithmic Thinking and Problem Decomposition
  • Sliding Window, Two Pointer, and Recursion Patterns
  • Graph Algorithms – Dijkstra, Floyd-Warshall, MST (Kruskal/Prim)
  • String Algorithms – KMP, Rabin Karp, Z-Algorithm
  • System Design Thinking for Large Data Problems
  • Mock Tests and Interview Simulation
  • Capstone Project: Build DSA Visualizer or Problem Tracker

Tools: LeetCode, HackerRank, Codeforces, GitHub

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