Dsa Progess


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Things To Remember

    ✅ Understand the logic, don’t just memorize code. 🧱 Always begin with brute force, then optimize. ✍️ Practice coding on paper—train for interviews. ⏱️ Time yourself like in real coding rounds. 🐞 Debug smartly—use print and dry runs. 🔁 Revise tricky questions every week. 📌 Never skip edge test cases. 🔍 Reflect on alternate solutions after solving. 🚀 Solve fewer, but with full depth. ⛔ Don’t rush to watch the solution—try first.

Daily Basiss Dsa Motivation

ARRAY

🔢 Quick Revise – Arrays

✅ What is an Array

  • A linear data structure.
  • Stores elements in contiguous memory.
  • Accessed via indexing (0-based).
  • Stores same type of elements.

🧠 Key Characteristics:

  • Fixed size (static arrays).
  • Elements stored in sequence.
  • Direct access via index: arr[i].
  • Efficient read, costly middle insert/delete.

⏱️ Time Complexities:

  • Access: O(1)
  • Update: O(1)
  • Search: O(n)
  • Insert/Delete (middle): O(n)

🧪 Important Techniques:

  • Two Pointers – optimize search & traversal.
  • Sliding Window – handle fixed or variable size subarrays.
  • Prefix Sum – for range-based calculations.
  • Kadane’s Algorithm – max subarray sum.
  • In-place Swapping / Reversal – optimize space.
  • Sorting + Binary Search – combo for many problems.

🧰 Language Helpers:

  • C++: vector, .size(), sort().
  • JavaScript: push(), splice(), map(), filter().

🎯 One-Shot: Master Arrays – Basics + Questions in One Go!

🧠 DSA That Gets You Hired – 0

TWO SUM

Best Time to Buy & Sell Stock

Maximum Subarray (Kadane’s Algo)

Move Zeroes

Contains Duplicate

Product of Array Except Self

Maximum Product Subarray

Maximum Subarray Min-Product

Longest Consecutive Sequence

Subarray Sum Equals K

Merge Intervals

Spiral Matrix

Set Matrix Zeroes

Missing Number

Majority Element

STRING

🧵 Quick Revise – Strings

✅ What is a String

  • A sequence of characters.
  • Stored as arrays in memory.
  • Strings are immutable in most languages.
  • Can be manipulated using standard libraries.

🧠 Key Characteristics:

  • Fixed or dynamic in size (depends on language).
  • 0-based indexing for character access.
  • Immutability in Java, Python (new copy on change).
  • Character operations like comparison, slicing, appending.

⏱️ Time Complexities:

  • Access: O(1)
  • Concatenation: O(n)
  • Search: O(n)
  • Substring: O(k) [k = substring length]

🧪 Important Techniques:

  • Two Pointers – useful in palindrome and pattern match.
  • Sliding Window – for substring search, longest unique substring.
  • Hashing – frequency/count maps for anagram problems.
  • Trie – prefix matching, auto-complete.
  • KMP / Z Algorithm – advanced pattern matching.

🧰 Language Helpers:

  • C++: string, substr(), compare(), find().
  • JavaScript: length, split(), indexOf(), slice(), includes().
  • Python: strip(), join(), find(), slicing, in-operator.

🎯 One-Shot: String Mastery – Concepts + Coding

🎯 String most important interview question

🧠 DSA That Gets You Hired – 0

Valid Anagram

Group Anagrams

Valid Palindrome

Longest Substring Without Repeating

Reverse Words in a String

Longest Palindromic Substring

Implement strStr()

Is Subsequence

Decode Ways

Edit Distance

SEARCHING

🔍 Quick Revise – Searching

✅ What is Searching?

  • Searching is the process of finding a target element within a data structure.
  • Two main types: Linear Search and Binary Search.
  • Binary Search works only on sorted data.

🧠 Key Concepts:

  • Linear Search: Sequential scan (O(n)).
  • Binary Search: Divide & conquer (O(log n)).
  • Variants: First/Last Occurrence, Count of Occurrences.
  • Advanced: Search in Rotated Array, Binary Search on Answer.

⏱️ Time Complexities:

  • Linear Search: O(n)
  • Binary Search: O(log n)

🧰 Language Helpers:

  • C++: std::binary_search(), lower_bound(), upper_bound()
  • JavaScript: indexOf(), findIndex(), includes()

🔎 Linear Search Explained in C++

🧭 Binary Search Mastery in C++

🧠 DSA That Gets You Hired – 0

Linear Search

Binary Search

Search in Rotated Sorted Array

First & Last Occurrence

Search Insert Position

Peak Index in Mountain Array

Find Peak Element

Single Element in Sorted Array

Find Min in Rotated Sorted

Find K Closest Elements

Koko Eating Bananas

Ship Packages Within D Days

Min Days to Make Bouquets

Split Array – Largest Sum

Matrix Binary Search

Smallest Letter > Target

Median of Two Sorted Arrays

Find Duplicate Number

Missing Number

Intersection of Two Arrays

Floor of Element

Ceil of Element

First Bad Version

Sqrt(x)

Nth Root of M (Binary Search)

SORTING

🔃 Quick Revise – Sorting

✅ Why Sorting is Important?

  • Sorting arranges data in a particular order (ascending/descending).
  • It is often used before searching, binary search, or optimization problems.

🧠 Key Sorting Algorithms:

  • Bubble Sort – swap adjacent elements repeatedly.
  • Selection Sort – select minimum & place at front.
  • Insertion Sort – build sorted part one element at a time.
  • Merge Sort – divide & merge (recursive).
  • Quick Sort – choose pivot and partition.

⏱️ Time Complexities:

  • Bubble, Insertion, Selection – O(n²)
  • Merge, Quick (Avg) – O(n log n)

🔁 Bubble , Selection ,Insertion Sort in C++

🧮 Merge Sort in C++

⚡ Quick Sort in C++

🧠 DSA That Gets You Hired – 0

Sort Colors (Dutch Flag)

Merge Intervals

Sort an Array

Largest Number

Wiggle Sort

Merge Sorted Array

Maximum Gap

Insertion Sort List

RECURSION & BACKTRACKING

🔄 Quick Revise – Recursion & Backtracking

✅ Key Concepts

  • Recursion: Function calling itself for sub-problems.
  • Base Case: Stopping condition for recursion.
  • Backtracking: Explore all paths, undo steps when needed.
  • Used in combinations, permutations, pathfinding, N-Queens, etc.

🧠 Tips

  • Always define base case.
  • Dry run small input.
  • Use recursion tree to debug.

🎯 Master Recursion & Backtracking in C++

🧠 DSA That Gets You Hired – 0

Subsets

Subsets II

Permutations

Permutations II

Combination Sum

Combination Sum II

Phone Number Letter Combinations

Generate Parentheses

Word Search

Palindrome Partitioning

N-Queens

N-Queens II

Sudoku Solver

Rat in a Maze

Restore IP Addresses

Unique Paths III

Beautiful Arrangement

Combination Sum III

Find All Increasing Subsequences

STACK

📚 Quick Revise – Stack

✅ What is a Stack?

  • Linear data structure that follows LIFO (Last-In-First-Out).
  • Insertion (push) and deletion (pop) at the same end (top).

🔧 Applications of Stack:

  • Expression evaluation and syntax parsing.
  • Undo mechanisms in editors.
  • Backtracking problems (recursion, DFS).

📦 Stack in C++ – Full Concept + Problems

🧠 DSA That Gets You Hired – 0

Valid Parentheses

Min Stack

Largest Rectangle in Histogram

Evaluate Reverse Polish Notation

Daily Temperatures

Next Greater Element I

Next Greater Element II

Remove Adjacent Duplicates

Score of Parentheses

Implement Stack using Queues

Implement Queue using Stacks

Online Stock Span

Remove K Digits

Basic Calculator II

Baseball Game

QUEUE

🚦 Quick Revise – Queue

✅ What is a Queue?

  • Linear data structure following FIFO (First-In-First-Out).
  • Insertion at rear, deletion from front.

🔧 Applications of Queue:

  • Task scheduling, CPU scheduling.
  • Print queues, messaging systems.
  • Level order traversal in trees.

📥 Queue in C++ – Concept + Implementation

🧠 DSA That Gets You Hired – 0

Implement Queue using Stacks

Implement Stack using Queues

Number of Recent Calls

Dota2 Senate

Rotting Oranges

Open the Lock

Perfect Squares

Shortest Path in Binary Matrix

Course Schedule

Time to Inform All Employees

K Closest Points to Origin

Sliding Window Maximum

Max Depth of Binary Tree

Find Winner of Circular Game

Last Stone Weight

LINKED LIST

🔗 Quick Revise – Linked List

✅ What is a Linked List?

  • Linear data structure where elements (nodes) point to the next.
  • Efficient insert/delete, dynamic size.
  • Types: Singly, Doubly, Circular.

📌 Key Operations:

  • Insertion & Deletion at head, tail, middle.
  • Finding middle, detecting/removing loops.
  • Reversing list, merging, sorting, etc.

🔗 Linked List in C++ – Full Tutorial + Questions

🧠 DSA That Gets You Hired – 0

Reverse Linked List

Middle of Linked List

Merge Two Sorted Lists

Remove Nth Node from End

Linked List Cycle

Linked List Cycle II

Intersection of Two Linked Lists

Add Two Numbers

Palindrome Linked List

Reorder List

Rotate List

Swap Nodes in Pairs

Remove Duplicates from Sorted List

Delete Node in a Linked List

Flatten Multilevel Doubly List

BINARY TREE

🌳 Quick Revise – Binary Tree

✅ What is a Binary Tree?

  • Each node has at most 2 children: left and right.
  • Special cases: full, perfect, complete, skewed trees.

📌 Applications:

  • Expression parsing, hierarchical storage, routing tables.
  • Foundations of BST, Heaps, Segment Trees.

🌳 Binary Tree in C++ (Traversals + Problems)

🧠 DSA That Gets You Hired – 0

Inorder Traversal

Preorder Traversal

Postorder Traversal

Level Order Traversal

Max Depth of Binary Tree

Symmetric Tree

Path Sum

Diameter of Binary Tree

Invert Binary Tree

Construct from Preorder + Inorder

Flatten Binary Tree

Right Side View

Check if Tree is Complete

LCA in Binary Tree

Zigzag Level Order

BINARY SEARCH TREE

🌲 Quick Revise – Binary Search Tree

✅ What is a BST?

  • A binary tree where left < root < right.
  • All left subtree nodes are smaller, right are greater.
  • Provides efficient search, insert, delete.

📌 Key Uses:

  • Dynamic sets, searching, and sorting.
  • Foundation for AVL, Red-Black Trees.

🌲 Binary Search Tree in C++ – Concept + Problems

🧠 DSA That Gets You Hired – 0

Search in BST

Insert into BST

Delete Node in BST

Kth Smallest in BST

Validate BST

LCA of BST

Range Sum of BST

Convert BST to Greater Tree

Trim a BST

Inorder Successor in BST

BST to Greater Sum Tree

Balance a BST

Min Absolute Diff in BST

BST to DLL

Find Mode in BST

GRAPH

🌐 Quick Revise – Graphs

✅ What is a Graph?

  • A collection of nodes (vertices) and edges.
  • Can be directed/undirected, weighted/unweighted.
  • Represented using adjacency list or matrix.

🔧 Traversal Techniques:

  • BFS – Level-order traversal using queue.
  • DFS – Depth-first using stack/recursion.
  • Cycle detection (DFS/Union Find).

🌐 Graph Basics in C++ – BFS, DFS, Cycles

🧠 DSA That Gets You Hired – 0

Number of Provinces (DFS)

Keys and Rooms

Course Schedule (Cycle Detection)

Clone Graph

Graph Valid Tree

Redundant Connection (Union Find)

Is Graph Bipartite?

Rotting Oranges (Multi-source BFS)

Max Distance from Land

Walls and Gates

Shortest Path in Binary Matrix

Find if Path Exists

Find the Town Judge

All Paths From Source to Target

Detect Cycle in Directed Graph

DYNAMIC PROGRAMMING

⚡ Quick Revise – Dynamic Programming

✅ What is Dynamic Programming?

  • Solving problems by breaking them into overlapping subproblems.
  • Use of memoization or tabulation to store results.
  • Reduces time complexity from exponential to polynomial.

🧠 Common Types:

  • 0/1 Knapsack, Subset Sum
  • Fibonacci, Climbing Stairs
  • Longest Common Subsequence (LCS)
  • Matrix DP, DP on Trees/Graphs

⚡ DP Made Easy – Complete C++

🧠 DSA That Gets You Hired – 0

Fibonacci Number

Climbing Stairs

House Robber

House Robber II

Longest Palindromic Substring

Partition Equal Subset Sum

Coin Change

Coin Change II

Longest Increasing Subsequence

Edit Distance

Distinct Subsequences

Target Sum

Minimum Path Sum

Unique Paths

Triangle (Min Path Sum)

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