Learn Data Structure
Using JavaScript

Declaring and invoking functions, Parameters and default values, Function expressions and anonymous functions, Function scope and return statement, Immediately Invoked Function Expressions (IIFE), Arrays, Declaring and initializing arrays, Common array methods: push, pop, shift, unshift, splice, slice, concat, join, etc., Iterating over arrays using for and forEach, Multidimensional arrays, Objects, Declaring objects and accessing properties, Adding, updating, and deleting properties, Nested objects and deep access, Iterating over object keys and values using for...in, Strings, String manipulation and methods, length, toUpperCase, toLowerCase, charAt, substring, slice, indexOf, lastIndexOf, split, replace, includes.

What is Complexity Analysis?, Importance in algorithm design and optimization, Role in real-world applications; Overview of Asymptotic Notation, Introduction to Big O, Ω (Big Omega), and Θ (Theta) notations, Differences and use cases of each notation; Big O Notation (Time Complexity Analysis), Understanding Time Complexity - Best, Worst, and Average Case Analysis, Practical examples with JavaScript code; Analyzing Iterative Algorithms - Single loops, nested loops, and dependent loops, Calculating time complexity step-by-step; Analyzing Recursive Algorithms - Deriving recurrence relations, Solving using recursion trees and the Master Theorem, Examples: Factorial, Fibonacci; Time Complexity Classes - Constant Time (O(1)), Linear Time (O(n)), Logarithmic Time (O(log n)), Linearithmic Time (O(n log n)), Quadratic Time (O(n²)), Exponential Time (O(2^n)), Examples of algorithms for each complexity class;; Understanding Space Complexity - Importance in resource-constrained environments, Static memory (variables, constants) vs. dynamic memory (data structures, recursion stack); Memory Usage Analysis - RAM usage in arrays and objects, Stack and heap memory in JavaScript; Space Complexity in Recursive Functions - Stack space and its role in recursion, Tail-call optimization in JavaScript; Practical Examples - Memory optimization techniques, Analyzing space complexity of common data structures (arrays, linked lists, hash maps); Common Time Complexities, Detailed Study - Characteristics of O(1), O(n), O(log n), O(n log n), O(n²), O(2^n), Real-world examples and algorithms for each; Visualizing Growth - Graphical representations of different complexities, Practical demonstrations using JavaScript performance measurement tools (e.g., console.time()); Amortized Analysis - Concept and importance, Use cases in real-world algorithms; Techniques for Amortized Analysis - Aggregate analysis, Accounting method, Potential method; Examples in JavaScript - Dynamic array resizing, Hash table operations, Incremental algorithms; Practical Applications - Implementing efficient data structures with amortized analysis (e.g., stacks, queues, dynamic arrays).

Basic String Operations - indexOf, includes, slice, split, replace, join, startsWith, endsWith, charAt, charCodeAt, Combining and formatting strings (concat, template literals, toUpperCase, toLowerCase); Reversing Strings - Iterative and recursive methods, Using JavaScript methods: split, reverse, join; Palindrome Checking - Methods for checking single strings, Optimized two-pointer approach; Anagram Detection - Sorting and comparing strings, Using frequency counts (hash maps); String Comparison - ASCII value comparison, Case-insensitive comparison using localeCompare; Advanced Manipulations - Trimming and padding strings (trim, trimStart, trimEnd, padStart, padEnd), Removing extra spaces between words, Capitalizing the first letter of each word, Counting the frequency of characters in a string; Sliding Window for Strings, Concepts of Sliding Window - Explanation with simple examples, Difference between fixed and variable size windows; Practical Problems - Longest substring without repeating characters, Smallest substring containing all characters of another string, Count occurrences of anagrams in a string, Find repeated substrings of a given length; Optimized Sliding Window - Using hash maps for fast lookups, Dynamic window resizing with conditions; Regular Expressions in JavaScript, Basics of Regex - Defining patterns using /pattern/, Special characters: ^, $, ., *, +, ?, [], (), Pattern Matching - Finding substrings with match, test, Validating email addresses, phone numbers, and passwords; Practical Usage - Extracting numbers from strings, Replacing multiple spaces with a single space, Removing non-alphanumeric characters, Validating user inputs in forms; String Hashing, Basics of Hashing - Hash functions for strings, Use cases in solving string problems; Rabin-Karp Algorithm - Pattern matching using hashing, Simplified implementation for beginners; Rolling Hash - Concept of sliding hash, Applications in substring matching problems ; Practical Implementations - Finding all duplicate substrings of a fixed length, Efficient substring comparison using hashing; String Parsing - Extracting data from delimited strings (CSV, TSV), Parsing JSON strings into objects; String Formatting - Padding numbers with leading zeros, Formatting dates and times using strings; Character Manipulation - Counting vowels and consonants in a string, Replacing vowels with special characters; Practical String-Based Problems - Checking for balanced parentheses in expressions, Removing duplicate words from a sentence, Finding the most frequent word in a paragraph.

Array introduction and properties, Operations: Insertion, Deletion, Accessing elements, Built-in methods in JavaScript: push, pop, shift, unshift, splice, etc.; Array-Based Problems - Two Sum, Three Sum problems; Subarray Problems: Maximum Subarray (Kadane’s Algorithm), Minimum Subarray; Sliding Window Techniques: Fixed-size sliding window, Variable-size sliding window; Majority Element Problems; Merge Intervals: Overlapping intervals, merging intervals; Advanced Techniques - Kadane’s Algorithm for maximum subarray sum, Prefix Sum Array for range sum queries, Binary Search in Arrays, Array rotations, Reverse Pairs; Multi-Dimensional Arrays - Matrix operations: Transpose, Rotation, Searching in a matrix (2D Binary Search), Spiral Matrix Traversal; Sparse Arrays - Concept and implementation, Optimization for sparse data storage and retrieval;; Linked Lists, Singly Linked Lists - Implementation from scratch, Insertion, Deletion, and Traversal, Middle of a Linked List, Cycle Detection (Floyd’s Cycle Detection); Doubly Linked Lists - Insertion, Deletion, and Traversal, Applications in real-world scenarios; Advanced Operations - Reverse a Linked List (Iterative and Recursive approaches), Merge Two Sorted Linked Lists, Flatten a Linked List, Implementing an LRU Cache using Linked List, Adding Two Numbers represented as Linked Lists, Reordering a List (odd-even reordering, alternating order); Circular Linked Lists - Introduction and applications, Implementation and traversal techniques.

Stack Basics, Operations: Push, Pop, Peek, Implementation using arrays and linked lists, Real-world applications; Applications of Stacks - Valid Parentheses check, Min Stack implementation, Largest Rectangle in Histogram, Next Greater Element problem, Simplify Path (Unix-style path simplification), Evaluate Reverse Polish Notation (RPN), Balanced Parentheses check; Monotonic Stacks - Concepts and implementation, Applications in interval-based problems;; Queue Basics, Operations: Enqueue, Dequeue, Implementation using arrays and linked lists, Real-world applications; Types of Queues - Circular Queue, Double-Ended Queue (Deque), Priority Queue implementation using heaps; Applications of Queues - Implementing a Stack using Queue, Sliding Window Maximum, BFS traversal using Queue, LRU Cache implementation using Queue, Task Scheduler problem, Rotten Oranges problem (multi-source BFS).

What is recursion? Key characteristics and when to use it, The stack mechanism in recursion: understanding call stacks, Visualization of recursive function calls and stack unwinding; Types of Recursion - Tail Recursion vs. Non-Tail Recursion, Identifying tail-recursive functions, Converting non-tail recursion into tail recursion, Examples: Factorial, Fibonacci series, Sum of n numbers; Key Components of Recursion - Base Case and Recursive Case, Importance of base cases in terminating recursion, Structuring recursive logic with practical examples; Recursion with Memoization - Optimizing recursive calls to avoid recomputation, Examples: Fibonacci series with memoization, climbing stairs problem, Use of JavaScript objects and arrays for memorization; Common Recursive Problems - Binary Search, Power of a number (x^n), GCD and LCM calculation, Printing subsets of a string or array; Introduction to Backtracking - Concept and how it differs from brute force, Role of constraints and pruning in backtracking, Understanding decision trees in backtracking; Framework for Backtracking - Writing general backtracking algorithms, Managing state and undoing choices (using arrays and objects); Key Backtracking Techniques - Generating all possible solutions, Pruning branches to optimize performance, Recursive vs. iterative backtracking; Backtracking Problems, Classic Problems - N-Queens Problem (Placing N queens on an NxN chessboard without conflicts., Recursive approach with pruning), Rat in a Maze (Finding all paths from source to destination in a grid, Handling blocked cells); Subsets and Variations - Generating all subsets of a set, Handling duplicates in subsets (Subsets II), Implementing power sets using backtracking; Permutations and Variations - Generating all permutations of an array or string, Handling duplicates in permutations (Permutations II); Combinations - Generating combinations of a given length, Solving combination sum problems (e.g., finding subsets that sum to a target value).

Introduction to Hash Maps and Hash Tables, Implementation of Hash Table, Collision Handling (Chaining, Open Addressing); Applications of Hashing - Frequency Count Problems, Longest Substring Without Repeating Characters, Longest Consecutive Sequence, Subarray Sum Equals K, Top K Frequent Elements, Group Anagrams, Happy Number;; Introduction to Tree, Binary Tree - Traversal Techniques (Preorder, Inorder, Postorder), Iterative vs Recursive Traversal, Level Order Traversal (BFS), Height and Depth of Binary Tree, Diameter of Binary Tree, Balanced Binary Trees (AVL Trees), Tree Path Problems (Root to Leaf), Binary Tree Upside Down, Flatten Binary Tree to Linked List; Binary Search Trees (BST) - Insertions and Deletions in BST, Find Minimum and Maximum, Inorder Successor and Predecessor, Validate BST, Lowest Common Ancestor in BST, Kth Smallest Element;; Graphs - Graph Representations, Adjacency Matrix, Adjacency List, Graph Traversal (DFS, BFS), Types of Graphs - Directed, Undirected, Cyclic, Acyclic; Graph Algorithms - Depth First Search (DFS), Breadth First Search (BFS), Topological Sorting (Kahn’s Algorithm), Dijkstra's Algorithm (Shortest Path), Bellman-Ford Algorithm, Floyd-Warshall Algorithm, Prim’s and Kruskal’s Algorithm for MST.

Characteristics of Greedy Algorithms, Greedy Algorithm Problems - Activity Selection Problem, Fractional Knapsack Problem, Minimum Number of Platforms, Minimum Coins Problem, Job Sequencing Problem, Huffman Coding, Interval Scheduling; Introduction to Dynamic Programming - Memoization and Tabulation, 1D, 2D DP Array Setup, Optimization with Space Reduction, Common DP Problems - Fibonacci Numbers, Climbing Stairs, Longest Increasing Subsequence, Longest Common Subsequence, Knapsack Problem (0/1 Knapsack, Fractional Knapsack), Coin Change, Decode Ways, Edit Distance, Minimum Path Sum, Unique Paths, Palindromic Substrings, Wildcard Matching, Maximum Product Subarray.

Heaps & Priority Queues, Min Heap and Max Heap Implementations - Heapify, Insert, and Delete Operations; Applications of Heaps - Merge K Sorted Lists, Kth Largest/Smallest Element, Top K Frequent Elements, Sliding Window Maximum using Heaps, Median of Data Stream, Heap Sort Algorithm, Job Scheduling; Sorting Algorithms - Merge Sort, Quick Sort, Counting Sort, Radix Sort; Searching Algorithms - Binary Search Variants, Ternary Search; Randomized Algorithms - Reservoir Sampling, Randomized Quick Sort; Mathematical Problems - GCD and LCM, Prime Numbers (Sieve of Eratosthenes) - Modular Arithmetic, Fibonacci Numbers in Logarithmic Time; Advanced Algorithms - Divide and Conquer Algorithms, Sliding Window Algorithms, Two Pointer Technique.