DSA Mastery Workspace

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⚡ DAILY CHALLENGE

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Easy LeetCode ⏱ ~15 mins
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Pomodoro Workspace

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"Stay focused, one problem at a time."

DSA Problem Solving Roadmap

Step-by-step interactive learning path from fundamentals to advanced algorithms

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Phase 1 • Problem-Solving Foundations 🎯 Practice: 30–40 Problems

Math, Logic & Basic Programming

Build the thinking skills required before learning advanced data structures.

🎯 Goal: Learn to convert a real-world question → steps → algorithm → code.
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Basic Math Problems Digit Sum • Count Digits • Reverse Number • Palindrome Number • Armstrong Number • Prime Number • GCD & LCM
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Loops & Patterns Nested Loops • Number Patterns • Star Patterns • Counting • Series • Basic Simulation
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Logical Problem Solving Maximum/Minimum • Even/Odd • Factors • Divisibility • Frequency • Simple Conditions
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Complexity Basics O(1) • O(log n) • O(n) • O(n²) • Time vs Space
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Phase 2 • Core Data Structures 🎯 Practice: 40–50 Problems

Arrays, Strings & Hashing

Learn how to store, access, search, and organize information efficiently.

🎯 Goal: Understand what data structure fits the problem.
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Arrays Traversal • Searching • Insertion • Deletion • Reverse • Rotation • Subarrays
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Strings Character Counting • Reverse • Palindrome • Anagram • Substrings
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Hashing Hash Map • Hash Set • Frequency Map • Duplicate Detection • Fast Lookup
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Essential Problems Contains Duplicate • Valid Anagram • Two Sum • Majority Element • Group Anagrams
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Phase 3 • Problem-Solving Patterns 🎯 Practice: 50–70 Problems

Two Pointers, Sliding Window, Prefix Sum & Binary Search

Start recognizing patterns instead of solving every problem from scratch.

🎯 Goal: Learn to recognize patterns that reduce unnecessary work.
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Two Pointers Opposite Direction • Same Direction • Pair Problems • Sorted Arrays • Palindrome
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Sliding Window Fixed Window • Variable Window • Longest Subarray • Longest Substring
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Prefix Sum Range Sum • Subarray Sum • Cumulative Information
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Binary Search Normal Search • First/Last Position • Rotated Array • Search Space • Binary Search on Answer
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Phase 4 • Linear & Recursive Structures 🎯 Practice: 50–70 Problems

Linked Lists, Stack, Queue & Recursion

Learn pointer manipulation and recursive thinking.

🎯 Goal: Learn to break a large problem into smaller manageable problems.
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Linked Lists Singly List • Doubly List • Reverse List • Middle Node • Fast & Slow Pointer • Cycle Detection
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Stack & Queue LIFO • FIFO • Valid Parentheses • Min Stack • Monotonic Stack • Next Greater Element
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Recursion Base Case • Recursive Call • Call Stack • Factorial • Fibonacci • Array Recursion
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Backtracking Intro Choose • Explore • Undo • Subsets
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Phase 5 • Non-Linear Data Structures 🎯 Practice: 60–80 Problems

Trees, Heaps & Graphs

Learn to work with hierarchical and connected data.

🎯 Goal: Learn how to traverse, explore, and process complex relationships.
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Trees & BST DFS • BFS • Preorder • Inorder • Postorder • Level Order • Height • Search
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Heaps & Priority Queue Min Heap • Max Heap • Kth Largest • Top K • Task Scheduling
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Graphs Adjacency List • BFS • DFS • Connected Components • Cycle Detection
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Graph Algorithms Shortest Path • Topological Sort • Union Find • MST Basics
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Phase 6 • Advanced Problem Solving 🎯 Practice: 80–100 Problems

Backtracking, Dynamic Programming & Interview Thinking

Solve problems where brute force is too expensive.

🎯 Goal: Stop asking “Have I seen this exact problem?” and start asking “What idea can solve this?”
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Backtracking Permutations • Combinations • Combination Sum • N-Queens • Maze Problems
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1D Dynamic Programming Climbing Stairs • House Robber • Coin Change • Decode Ways • LIS
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2D Dynamic Programming Grid Problems • Knapsack • LCS • Subsequence Problems
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DP Thinking Recursion → Identify State → Memoization → Tabulation → Space Optimization
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Interview Problem Solving Understand → Example → Brute Force → Find Bottleneck → Optimize → Code → Test → Complexity

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