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Module 1: Introduction to DSA and Core Concepts

• 1.1: What, Why DSA

  • What Are Data Structures?
  • What Are Algorithms?
  • Why Learn DSA?

• 1.2: Time and Space Complexity

  • Big O Notation: An Introduction
  • Analyzing Time Complexity
  • Analyzing Space Complexity
  • Best, Average, and Worst Case Scenarios

• 1.3: Course Navigation and Resources

  • How to Use This Course
  • Solutions and Code Examples

Module 2: Foundational Data Structures

• 2.1: Arrays

  • Array Definition & JavaScript Arrays
  • Basic Array Operations (Insert, Delete, Access)
  • Challenge: Manual Array Insertion
  • Challenge: Manual Array Deletion
  • Multi-dimensional Arrays
  • Challenge: Rotate N×N Matrix by 90°

• 2.2: Strings

  • String Basics & Immutability
  • Common String Methods & Their Complexity
  • Challenge: String Reversal
  • Challenge: Palindrome Checker
  • Challenge: Anagram Detector

• 2.3: Linked Lists

  • Node and Linked List Structure
  • Types of Linked Lists (Singly, Doubly, Circular)
  • Linked List Operations (Insertion, Deletion, Traversal)
  • Challenge: Detect a Cycle in a Linked List
  • Challenge: Merge Two Sorted Linked Lists
  • Challenge: Reverse a Linked List

• 2.4: Sets

  • Set Definition & Characteristics
  • JavaScript `Set` Object & Basic Operations
  • Set Operations (Union, Intersection, Difference)
  • Set Use Cases & Time Complexity
  • Challenge: Find Unique Elements

Module 3: Abstract Data Types (ADTs)

• 3.1: Stacks

  • Stack Theory: LIFO Principle
  • Stack Implementations (Array-based, Linked List-based)
  • Stack Applications (Call Stack, Undo/Redo)
  • Challenge: Balanced Parentheses

• 3.2: Queues

  • Queue Theory: FIFO Principle
  • Queue Implementations (Array-based, Linked List-based)
  • Queue Applications (Task Scheduling, BFS)
  • Challenge: Implement a Queue

• 3.3: Hash Tables

  • Hashing Concepts and Principles
  • Collision Resolution Techniques (Chaining, Open Addressing)
  • Hash Table Operations (Insert, Delete, Search)
  • JavaScript Map and Object as Hash Tables
  • Challenge: Two Sum

Module 4: Trees

• 4.1: Introduction to Trees

  • Tree Terminology (Root, Node, Leaf, Depth, Height)
  • Types of Trees (General Tree, Binary Tree)

• 4.2: Binary Trees

  • Binary Tree Properties and Characteristics
  • Binary Tree Traversals (Inorder, Preorder, Postorder, BFS)

• 4.3: Binary Search Trees (BSTs)

  • Introduction to BSTs
  • BST Properties and Rules
  • BST Operations (Insert, Delete, Search)

• 4.4: Heaps (Priority Queues)

  • Heap Properties and Types (Min-Heap, Max-Heap)
  • Heap Operations (Insert, Extract-Min/Max, Heapify)
  • Heap Applications (Priority Queues, Heap Sort)

• 4.5: Balanced Trees (Brief Introduction)

  • The Need for Balanced Trees (AVL, Red-Black - conceptual)
  • Challenge: Check if a Binary Tree is Balanced
  • Challenge: Implement AVL Rotations

• 4.6: Tries (Prefix Trees)

  • Trie Theory and Structure
  • Trie Operations and Applications (Autocomplete, Spell Checker)

Module 5: Graph Theory

• 5.1: Introduction to Graphs

  • Graph Terminology (Vertex, Edge, Directed, Weighted)
  • Graph Representations (Adjacency Matrix, Adjacency List)

• 5.2: Graph Traversal Algorithms

  • Breadth-First Search (BFS)
  • Depth-First Search (DFS)

• 5.3: Shortest Path Algorithms

  • Dijkstra's Algorithm
  • Bellman-Ford & Floyd-Warshall (Introduction)

• 5.4: Minimum Spanning Trees (MSTs)

  • Prim's Algorithm
  • Kruskal's Algorithm

• 5.5: Topological Sort

  • Topological Sort Algorithm and Applications

• 5.6: Disjoint Set Union (Union-Find)

  • Union-Find Operations and Data Structure

Module 6: Algorithmic Paradigms & Techniques

• 6.1: Recursion

  • Recursion Basics: Base Case & Recursive Step
  • Recursion Applications and Tracebacks

• 6.2: Searching Algorithms

  • Linear Search
  • Binary Search

• 6.3: Sorting Algorithms

  • Basic Sorts (Bubble, Selection, Insertion)
  • Efficient Sorts (Merge Sort, Quick Sort)
  • Non-Comparison Sorts (Radix, Counting - conceptual)

• 6.4: Divide and Conquer

  • Divide and Conquer Theory & Examples

• 6.5: Greedy Algorithms

  • Greedy Theory & Examples

• 6.6: Dynamic Programming

  • DP Theory: Overlapping Subproblems & Optimal Substructure
  • Memoization vs. Tabulation
  • Classic DP Problems (Fibonacci, Knapsack, Longest Common Subsequence)

• 6.7: Backtracking

  • Backtracking Theory
  • Classic Backtracking Problems (N-Queens, Sudoku Solver, Permutations)

Module 7: Advanced Topics & Interview Preparation

• 7.1: Bit Manipulation

  • Bitwise Operators in JavaScript
  • Bit Manipulation Applications (Flags, Optimization)

• 7.2: Advanced Data Structures (Introduction)

  • What and When to Use Advanced DS (Fenwick Tree, Segment Tree - conceptual)

• 7.3: Algorithm Design Patterns

  • Common Problem Solving Patterns (Sliding Window, Two Pointers, etc.)

• 7.4: DSA in Web Development

  • Real-World DSA Examples in Frontend/Backend

• 7.5: Technical Interview Strategies

  • Approaching Problems (Clarify, Examples, Brute Force, Optimize, Test)
  • Communication and Mock Interviews

Course Conclusion

• What's Next?

  • Next Steps in Your DSA Journey
  • Community and Continued Engagement