DSA Notes

Hub Algorithm vs Data Structure - Key Differences Real-World Applications of DSA Time and Space Complexity Overview What is DSA? Data Structures and Algorithms Why Learn Data Structures and Algorithms Asymptotic Analysis - Understanding Algorithm Growth Best, Average, and Worst Case Analysis Big O Notation - Upper Bound Analysis Big Omega Notation - Lower Bound Analysis Big Theta Notation - Tight Bound Analysis Space Complexity - Memory Analysis Time Complexity Examples and Analysis Array Interview Problems and Solutions Array Operations - Insert, Delete, Search Array Rotation Techniques Array Searching Techniques Arrays - Fundamental Data Structure Dynamic Arrays - Flexible Sizing Multidimensional Arrays - 2D and Beyond Sliding Window Technique for Arrays Anagram Problems - Character Rearrangement Palindrome Problems and Solutions Pattern Searching in Strings String Interview Problems Collection String Matching Algorithms - Pattern Searching String Operations - Concatenation, Substring, Manipulation String Data Structure - Fundamentals Circular Linked List - Ring Structure Doubly Linked List - Bidirectional Traversal Linked List Interview Problems Linked List - Data Structure Fundamentals Linked List Operations - Insert, Delete, Reverse Reverse Linked List - Iterative and Recursive Singly Linked List - Implementation and Operations Balanced Parentheses - Stack Application Infix, Prefix, Postfix Expressions - Stack Application Stack Implementation Techniques Stack Interview Problems and Solutions Stack Data Structure - LIFO Principle Stack using Arrays - Static Implementation Stack using Linked Lists - Dynamic Implementation Circular Queue - Efficient Space Usage Deque (Double Ended Queue)Priority Queue - Custom Ordering Queue Applications - Real-World Use Cases Queue Implementation - Array and Linked List Queue Interview Problems and Solutions Queue Data Structure - FIFO Principle AVL Tree - Self-Balancing Binary Search Tree Binary Search Tree (BST)Binary Tree - Structure and Operations Fenwick Tree (Binary Indexed Tree)Red-Black Tree - Another Balanced BST Segment Tree - Range Query Data Structure Tree Interview Problems Collection Tree Data Structure Fundamentals Tree Traversals - Inorder, Preorder, Postorder, Level Order Heap Data Structure Heap Operations Heap Interview Problems Max-Heap Min-Heap Implementation and Use Cases Priority Queue using Heap Bellman-Ford Algorithm Breadth-First Search (BFS) Algorithm Depth-First Search (DFS) Algorithm Dijkstra\Floyd-Warshall Algorithm Graph Interview Problems Graph Data Structure Fundamentals Graph Representation Methods Kruskal\Minimum Spanning Tree (MST)Prim\Shortest Path Algorithms: Complete Comparison Topological Sorting Union-Find (Disjoint Set Union)Separate Chaining Collision Handling Hash Functions Hashing Fundamentals Hashing Interview Problems Open Addressing Bubble Sort - Simple Sorting Algorithm Counting Sort - Non-Comparison Sorting Heap Sort - Heap Based Sorting Insertion Sort - Build Sorted Array Merge Sort - Divide and Conquer Quick Sort - Partition Based Sorting Radix Sort - Digit Based Sorting Selection Sort - Selection Based Sorting Sorting Algorithms Comparison - Choosing the Right Algorithm Sorting Algorithms - Fundamentals and Concepts Binary Search - Efficient Searching Interpolation Search - Intelligent Searching Linear Search - Sequential Search Algorithm Search Algorithms Comparison Searching Algorithm Interview Problems Ternary Search Algorithm Backtracking with Recursion Recursion Fundamentals - Base Case and Recurrence Recursion Problems and Solutions Recursion Tree - Visualizing Function Calls Recursion vs Iteration - Comparing Approaches Tail Recursion - Optimization Technique Dynamic Programming Interview Problems Dynamic Programming Fundamentals Fibonacci with Dynamic Programming 0/1 Knapsack Problem Longest Common Subsequence (LCS)Longest Increasing Subsequence (LIS)Matrix Chain Multiplication Memoization (Top-Down Dynamic Programming)Tabulation (Bottom-Up Dynamic Programming)Activity Selection Problem Fractional Knapsack Greedy Interview Problems Greedy Algorithm Fundamentals Huffman Coding — Data Structures & Algorithms Job Sequencing with Deadlines Backtracking Fundamentals Backtracking Practice Problems N-Queens Problem Rat in a Maze Subset Generation Sudoku Solver Codeforces Guide for Competitive Programmers Common Competitive Programming Techniques Contest Strategies for Competitive Programming Competitive Programming Introduction Fast Input/Output for Competitive Programming LeetCode Guide for Interview Preparation Bit Manipulation - Advanced Techniques Bloom Filter - Probabilistic Data Structure Computational Geometry - Algorithms and Techniques Disjoint Set Union (DSU) - Union Find Randomized Algorithms - Probabilistic Computing Skip List - Probabilistic Balanced Structure Suffix Array - String Indexing Trie - Prefix Tree for Efficient Searching Autocomplete System Using Trie Library Management System Using DSA Route Finder Using Graphs Mini Search Engine Project Social Network Analysis Using Graphs Essential Coding Patterns for Interviews DSA Cheatsheet: Quick Reference Essential DSA Interview Questions FAANG DSA Sheet: Company-Specific Patterns and Problems Problem Solving Strategies for Coding Interviews Complexity Cheatsheet Important Algorithms Summary Best Practice Platforms for DSA Recommended Books for DSA

FAANG DSA Sheet: Company-Specific Patterns and Problems

Company-specific DSA patterns for Amazon, Google, Meta, Apple, and Microsoft with 50+ curated problems organized by difficulty and category.

Company-Specific Patterns

Each FAANG company has distinct interviewing patterns based on their products, engineering culture, and what their teams work on daily. Understanding these patterns helps you prioritize your preparation.

Amazon Patterns

Focus areas: Trees, Graphs, BFS/DFS, Object-Oriented Design, Arrays

Amazon builds massive distributed systems, so they love problems involving tree/graph traversals (representing hierarchies, networks) and system design thinking. Their Leadership Principles also influence behavioral questions heavily.

Amazon Top Problems:

#	Problem	Difficulty	Category
1	Number of Islands	Medium	Graph/BFS
2	Rotting Oranges	Medium	BFS
3	Course Schedule	Medium	Topological Sort
4	Binary Tree Level Order Traversal	Medium	Tree/BFS
5	Lowest Common Ancestor	Medium	Tree
6	Word Ladder	Hard	BFS
7	Merge K Sorted Lists	Hard	Heap
8	LRU Cache	Medium	Design
9	Two Sum	Easy	Hash Map
10	Trapping Rain Water	Hard	Two Pointers
11	Maximum Subarray	Medium	DP/Kadane
12	Reorder Data in Log Files	Medium	Custom Sort

Amazon Tips:

Practice BFS variations extensively — multi-source BFS, shortest path in unweighted graphs
Know your tree traversals cold (pre/in/post order, level order)
Be prepared to design classes (OOP) as part of coding questions

Google Patterns

Focus areas: Dynamic Programming, Math/Number Theory, String Algorithms, Advanced Graphs

Google values mathematical thinking and optimization. Their problems tend to require clever observations and non-obvious DP formulations. Expect harder problems overall.

Google Top Problems:

#	Problem	Difficulty	Category
1	Longest Increasing Subsequence	Medium	DP
2	Word Break	Medium	DP
3	Decode Ways	Medium	DP
4	Regular Expression Matching	Hard	DP
5	Median of Two Sorted Arrays	Hard	Binary Search
6	Longest Valid Parentheses	Hard	Stack/DP
7	Count of Smaller Numbers After Self	Hard	Merge Sort
8	Alien Dictionary	Hard	Topological Sort
9	Split Array Largest Sum	Hard	Binary Search
10	Minimum Window Substring	Hard	Sliding Window
11	Serialize and Deserialize Binary Tree	Hard	Tree/DFS
12	Candy	Hard	Greedy

Google Tips:

Master DP on strings (edit distance, regex matching, word break)
Practice binary search on answer problems
Be comfortable with math-heavy problems (combinatorics, number theory)
Google often asks follow-up questions that increase complexity

Meta (Facebook) Patterns

Focus areas: Arrays, Strings, Binary Search, Intervals, Medium-difficulty Breadth

Meta interviews tend to have 2 questions per 45-minute session (unlike Google's 1 hard). This means problems are medium difficulty but you need speed. Arrays and strings dominate.

Meta Top Problems:

#	Problem	Difficulty	Category
1	Valid Palindrome II	Easy	Two Pointers
2	Move Zeroes	Easy	Two Pointers
3	Merge Intervals	Medium	Intervals
4	Subarray Sum Equals K	Medium	Prefix Sum
5	Product of Array Except Self	Medium	Arrays
6	Random Pick with Weight	Medium	Binary Search
7	Add Binary	Easy	Strings
8	Vertical Order Traversal	Hard	Tree/Sort
9	Binary Tree Right Side View	Medium	BFS
10	Lowest Common Ancestor (General)	Medium	Tree
11	Accounts Merge	Medium	Union Find
12	Buildings With an Ocean View	Medium	Stack

Meta Tips:

Speed matters — practice solving mediums in 15-20 minutes
Know interval problems inside and out
Binary search variations appear frequently
String manipulation is tested more often than at other companies

Apple Patterns

Focus areas: Arrays, Sorting, Two Pointers, Linked Lists, Clean Code

Apple emphasizes code quality and readability. They may ask you to refactor or optimize existing code. Problems tend toward medium difficulty with emphasis on clean implementation.

Apple Top Problems:

#	Problem	Difficulty	Category
1	3Sum	Medium	Two Pointers
2	Valid Parentheses	Easy	Stack
3	Merge Two Sorted Lists	Easy	Linked List
4	Container With Most Water	Medium	Two Pointers
5	Letter Combinations of Phone Number	Medium	Backtracking
6	Spiral Matrix	Medium	Arrays
7	Rotate Image	Medium	Matrix
8	Find Peak Element	Medium	Binary Search
9	Clone Graph	Medium	Graph/DFS
10	Top K Frequent Elements	Medium	Heap

Apple Tips:

Write clean, well-structured code with proper naming
Be prepared to handle follow-ups about edge cases
They value system design discussion even in coding rounds

Microsoft Patterns

Focus areas: Well-rounded (Arrays, Trees, Strings, DP, Graphs)

Microsoft interviews are the most balanced — no single category dominates. They test breadth of knowledge across all DSA topics at medium difficulty.

Microsoft Top Problems:

#	Problem	Difficulty	Category
1	Reverse Linked List	Easy	Linked List
2	Maximum Depth of Binary Tree	Easy	Tree
3	Valid BST	Medium	Tree
4	Group Anagrams	Medium	Hash Map
5	Longest Substring Without Repeats	Medium	Sliding Window
6	Coin Change	Medium	DP
7	Number of Islands	Medium	Graph
8	String to Integer (atoi)	Medium	String Parsing
9	Reverse Words in String	Medium	String
10	Excel Sheet Column Title	Easy	Math

Microsoft Tips:

Be well-rounded — do not over-specialize
They ask design questions (design a parking lot, design an elevator)
String parsing problems are more common here than at other companies

Study Plan: 4-Week FAANG Preparation

Week 1: Foundations

Complete all Easy problems from your target company's list
Review: Arrays, Strings, Hash Maps, Two Pointers
Daily: 3 problems (1 Easy, 2 Medium)

Week 2: Core Patterns

Medium problems: Trees, Graphs, BFS/DFS
Begin DP (1D problems: climbing stairs, coin change, house robber)
Daily: 2-3 Medium problems

Week 3: Advanced + Company-Specific

Hard problems from your target company
Advanced DP, Heap problems, Sliding Window
Start mock interviews (Pramp, interviewing.io)
Daily: 1-2 Hard problems + 1 Medium

Week 4: Mock + Review

Full mock interviews (timed, with communication practice)
Re-solve problems you struggled with from weeks 1-3
Review your notes on patterns and key insights
Rest the day before your interview

Exam Focus

Revise definitions, diagrams, examples, and short-answer points for FAANG DSA Sheet: Company-Specific Patterns and Problems.

Interview Use

Prepare one clear explanation, one practical example, and one common mistake for this Data Structures & Algorithms topic.

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