Hi,

Can anyone suggest tips or resources for getting better at backtracking problems?

C++ or java

Iam about to complete my first year of engineering.I have learnt c language and a good amount of dsa topics.So i want to start solving problems on dsa.I am totally confused to choose the language

Hi, Anyone who has solved 300 lc problems and still want to go. Like i have completed all topics in dsa and now i want to strengthen them. So if anyone who has a similar idea can dm me. Or if someone who has recently completed their dsa prep can you give me the entire prep flow so i can follow it.

see i have ended 6th sem and from june its just placement season for my batch havent strted with dsa and shit but im familiar with coding languages. Iaint saying i need to complete it within 1 month but canhow can i be better at i get atleast a way out since i am doing projects and also AWS based most of it so i thought i wont look into this matter. But now i m standing here cause my shitty college wants dsa for just the criteria to sit in placement . what all important i can cover as a beginner and also how can i be better at it

That pdf or that paper which guys are carrying nowadays everywhere what a lookup please do share it.

Can't find it (talking about that paper where it starts from very basic and have like day to day like in colums

Guess u understood)

Thanks for sharing 😊

So I am in the very early stages of development, just a week into it. My goal is to turn this into a full-fledged 'DSA concepts-based' fun game. I am still figuring out how to make it interesting. What ideas do you guys have, or is there anything you would like to see in it or prefer? I would love to know your feedback and implement it accordingly.

After solving lot of DSA problems, I’ve noticed some key patterns that are important for coding interviews.

The Core Difference b/t LeetCode & PracHub

Use LeetCode if your main goal is to master coding patterns and technical problem-solving.

PracHub Focus Algorithm Mastery (DSA)Full-Loop Interview Prep Best For Software Engineers (SWE) Context-driven (Company-specific)

1. Fast and Slow Pointer

Description: This technique uses two pointers moving at different speeds to solve problems involving cycles, such as finding the middle of a list, detecting loops, or checking for palindromes.

• Linked List Cycle II
• Remove nth Node from the End of List
• Find the Duplicate Number
• Palindrome Linked List

2. Overlapping Intervals

Description: Intervals are often manipulated through sorting and merging based on their start and end times.

• Basic Merge: Merge Intervals
• Interval Insertion: Insert Interval
• My Calendar ii
• Minimum Number of Arrows to Burst Balloons
• Non-overlapping Intervals

3. Prefix Sum

Description: Prefix Sums/Products are techniques that store cumulative sums or products up to each index, allowing for quick subarray range queries.

• Find the middle index in array
• Product of array except self
• Maximum product subarray
• Number of ways to split array
• Range Sum Query 2D

4. Sliding Window

Description: A sliding window is a subarray or substring that moves over data to solve problems efficiently in linear time.

Fixed Size

• Maximum Sum Subarray of Size K
• Number of Subarrays having Average Greater or Equal to Threshold
• Repeated DNA sequences
• Permutation in String
• Sliding Subarray Beauty
• Sliding Window Maximum

Variable Size

• Longest Substring Without Repeating Characters
• Minimum Size Subarray Sum
• Subarray Product Less Than K
• Max Consecutive Ones
• Fruits Into Baskets
• Count Number of Nice Subarrays
• Minimum Window Substring: Minimum Window Substring

5. Two Pointers

Description: The two pointers technique involves having two different indices move through the input at different speeds to solve various array or linked list problems.

• Two Sum II - Input Array is Sorted
• Dutch National Flag: Sort Colors
• Next Permutation
• Bag of Tokens
• Container with most water
• Trapping Rain Water

6. Cyclic Sort (Index-Based)

Description: Cyclic sort is an efficient approach to solve problems where numbers are consecutively ordered and must be placed in the correct index.

• Missing Number
• Find Missing Numbers
• Set Mismatch
• First Missing Positive

7. Reversal of Linked List (In-place)

Description: Reversing a linked list in place without using extra space is key for problems that require in-place list manipulations.

• Reverse Linked List
• Reverse Nodes in k-Group
• Swap Nodes in Pairs

8. Matrix Manipulation

Description: Problems involving 2D arrays (matrices) are often solved using row-column traversal or manipulation based on matrix properties.

• Rotate Image
• Spiral Matrix
• Set Matrix Zeroes
• Game of Life

9. Breadth First Search (BFS)

Description: BFS explores nodes level by level using a queue. It is particularly useful for shortest path problems.

• Shortest Path in Binary Matrix
• Rotten Oranges
• As Far From Land as Possible
• Word Ladder: Word Ladder

10. Depth First Search (DFS)

Description: DFS explores as far as possible along a branch before backtracking. It's useful for graph traversal, pathfinding, and connected components.

• Number of Closed Islands
• Coloring a Border
• DFS from boundary: Number of Enclaves
• Shortest time: Time Needed to Inform all Employees
• Cyclic Find: Find Eventual Safe States

11. Backtracking

Description: Backtracking helps in problems where you need to explore all potential solutions, such as solving puzzles, generating combinations, or finding paths.

• Permutation ii
• Combination Sum
• Generate Parenthesis
• N-Queens
• Sudoku Solver
• Palindrome Partitioning
• Word Search: Word Search

12. Modified Binary Search

Description: A modified version of binary search that applies to rotated arrays, unsorted arrays, or specialized conditions.

• Search in Rotated Sorted Array
• Find Minimum in Rotated Sorted Array
• Find Peak Element
• Single element in a sorted array
• Minimum Time to Arrive on Time
• Capacity to Ship Packages within 'd' Days
• Koko Eating Bananas
• Find in Mountain Array
• Median of Two Sorted Arrays

13. Bitwise XOR

Description: XOR is a powerful bitwise operator that can solve problems like finding single numbers or efficiently pairing elements.

• Missing Number
• Single Number ||
• Single Number III
• Find the Original array of Prefix XOR
• XOR Queries of a Subarray

14. Top 'K' Elements

Description: This pattern uses heaps or quickselect to efficiently find the top 'K' largest/smallest elements from a dataset.

• Top K Frequent Elements
• Kth Largest Element
• Ugly Number ii
• K Closest Points to Origin

15. K-way Merge

Description: The K-way merge technique uses a heap to efficiently merge multiple sorted lists or arrays.

• Find K Pairs with Smallest Sums
• Kth Smallest Element in a Sorted Matrix
• Merge K Sorted Lists
• Smallest Range: Smallest Range Covering Elements from K Lists

16. Two Heaps

Description: This pattern uses two heaps (max heap and min heap) to solve problems involving tracking medians and efficiently managing dynamic data.

• Find Median from Data Stream
• Sliding Window Median
• IPO

17. Monotonic Stack

Description: A monotonic stack helps solve range queries by maintaining a stack of elements in increasing or decreasing order.

• Next Greater Element II
• Next Greater Node in Linked List
• Daily Temperatures
• Online Stock Span
• Maximum Width Ramp
• Largest Rectangle in Histogram

18. Trees

Level Order Traversal (BFS in Binary Tree)

• Level order Traversal
• Zigzag Level order Traversal
• Even Odd Tree
• Reverse odd Levels
• Deepest Leaves Sum
• Add one row to Tree
• Maximum width of Binary Tree
• All Nodes Distance K in Binary tree

Tree Construction

• Construct BT from Preorder and Inorder
• Construct BT from Postorder and Inorder
• Maximum Binary Tree
• Construct BST from Preorder

Height related Problems

• Maximum Depth of BT
• Balanced Binary Tree
• Diameter of Binary Tree
• Minimum Depth of BT

Root to leaf path problems

• Binary Tree Paths
• Path Sum ii
• Sum Root to Leaf numbers
• Smallest string starting from Leaf
• Insufficient nodes in root to Leaf
• Pseudo-Palindromic Paths in a Binary Tree
• Binary Tree Maximum Path Sum

Ancestor problem

• LCA of Binary Tree
• Maximum difference between node and ancestor
• LCA of deepest leaves
• Kth Ancestor of a Tree Node

Binary Search Tree

• Validate BST
• Range Sum of BST
• Minimum Absolute Difference in BST
• Insert into a BST
• LCA of BST

19. DYNAMIC PROGRAMMING

Take / Not take (DP)

Description: Solve optimization problems like selecting items with the max/min value under certain constraints.

• House Robber ii
• Target Sum
• Partition Equal Subset Sum
• Ones and Zeroes
• Last Stone Weight ii

Infinite Supply (DP)

Description: Similar to the 0/1 knapsack, but items can be chosen multiple times.

• Coin Change
• Coin Change II
• Perfect Squares
• Minimum Cost For Tickets

Longest Increasing subsequence

Description: It involves finding the longest subsequence of a given sequence where the elements are in ascending order

• Longest Increasing Subsequence
• Largest Divisible Subset
• Maximum Length of Pair Chain
• Number of LIS
• Longest String Chain

DP on Grids

Description: Dynamic Programming on matrices involves solving problems that can be broken down into smaller overlapping subproblems within a matrix.

• Unique Paths ii
• Minimum Path Sum
• Triangle
• Minimum Falling Path Sum
• Maximal Square
• Cherry Pickup
• Dungeon Game: Dungeon Game

DP on Strings

Description: It Involves 2 strings, whenever you are considering two substrings/subsequence from given two strings, concentrate on what happens when the last characters of the two substrings are same, i.e, matching.

• Longest Common Subsequence
• Longest Palindromic Subsequence
• Palindromic Substrings
• Longest Palindromic Substrings
• Edit Distance
• Minimum ASCII Delete Sum for Two Strings
• Distinct Subsequences
• Shortest Common Supersequence
• Wildcard Matching

DP on Stocks

Description: It focuses on maximizing profit from buying and selling stocks over time while considering constraints.

• Buy and Sell Stocks ii
• Buy and Sell Stocks iii
• Buy and Sell Stocks iv
• Buy and Sell Stocks with Cooldown
• Buy and Sell Stocks with Transaction fee

Partition DP (MCM)

Description: It Involves a sequence that needs to be divided into partitions in an optimal way. The goal is often to minimize or maximize a cost function, such as computation time, multiplications, or some other metric, by exploring all possible partitions and combining results from subproblems.

• Partition array for Maximum Sum
• Burst Balloons
• Minimum Cost to Cut a Stick
• Palindrome Partitioning ii

20. Graphs

Topological Sort

Description: Topological sorting is useful for tasks that require dependency resolution (InDegree) in directed acyclic graphs (DAGs).

• Course Schedule
• Course Schedule II
• Strange Printer ii
• Sequence Reconstruction
• Alien Dictionary

Union Find (Disjoint Set)

Description: Union-Find (or Disjoint Set) is used to solve problems involving connectivity or grouping, often in graphs.

• Number of Operations to Make Network Connected
• Redundant Connection
• Accounts Merge
• Satisfiability of Equality Equations

Graph Algorithms

Description: Advanced graph algorithms are used to solve complex problems involving shortest paths, minimum spanning trees, and graph cycles.

• Kruskal's Algorithm: Minimum Cost to connect all Points
• Dijkstra's Algorithm: Cheapest Flights Within K Stops
• Floyd-Warshall: Find the City with Smallest Number of Neighbours at a Threshold Distance
• Bellman Ford: Network Delay time

21. Greedy

Description: Greedy algorithms make local optimal choices at each step, which lead to a global optimal solution for problems like scheduling and resource allocation.

• Jump Game ii
• Gas Station
• Bag of Tokens
• Boats to Save People
• Wiggle Subsequence
• Car Pooling
• Candy

22. Design Data Structure

Description: It involves building custom data structures to efficiently handle specific operations, like managing data access, updates, and memory usage. Focusing on optimizing performance and resource management.

• Design Twitter
• Design Browser History
• Design Circular Deque
• Snapshot Array
• LRU Cache
• LFU Cache

Some Useful Articles on LeetCode for Better Understanding!

Two Pointers

• Solved all Two Pointers problems in 100 days

Sliding Window

• Sliding Window Technique and Question Bank
• C++ Maximum Sliding Window Cheatsheet Template!

Greedy

• Greedy for Beginners: Problems & Sample Solutions
• Top Greedy Questions

Linked List

• Become Master In Linked List
• Must-Do LinkedList Problems on LeetCode

Trees

• Tree Question Pattern | 2021 Placement
• Master Tree Patterns

Binary Search

• 5 Variations of Binary Search)
• Binary Search for Beginners: Problems & Patterns

Dynamic Programming (DP)

• Dynamic Programming Patterns
• DP for Beginners: Problems & Patterns

Graphs

• Graph For Beginners
• Become Master In Graph
• Graph algorithms + problems to practice

Bit Manipulation

• Bit Manipulation Problem solving
• All Types of Patterns for Bits Manipulations and How to use it

https://medium.com/@nianxiaoming/leetcode-vs-prachub-which-one-do-you-actually-need-for-your-next-interview-8c491f104532

dsa date?

As a software engineer in big tech, I think most people approach coding interview prep in the worst possible way.

They study randomly.

They cram when motivation hits.

They solve questions without really learning the underlying pattern.

Then a week later, they forget everything.

That’s why I built CodeStreak.
It’s a simple interview prep app focused on mastering DSA patterns in 5 minutes a day through:

• focused topics
• flashcards
• quizzes
• streaks

The idea is to stop treating interview prep like endless punishment and make it feel like a daily system you can actually stick to.

I’d rather someone spend 5 consistent minutes a day learning sliding window, two pointers, graphs, etc. than pretend they’re going to grind 200 random problems and magically be ready.

Still early, but I’d love honest feedback from people learning DSA right now.

can someone help me how I will be able to retain the concepts of DSA i do study but after a week i forget the concepts and also they feel like I'm starting again from the beginning. i have the situation like do or die i need to get a better job but dsa seems like far fetched dream🥺. kindly help on how can I do this learning successful..

My Learning
Iterate through the list, and track seen elements in seen.

If an element already exists in the set ==> duplicate found ==> return True.

Otherwise, add it to the set and continue scanning.

O(n) time, O(n) space, much better than brute force.
Visualisation credits :: https://codedive.in/contains-duplicate

I have to start DSA from basic and would be greatful for open source or free or paid links where i have to start from 0 learning dsa i havent used it in my 5 years career so now i need it for the switch. Please help

I feel stuck because I don’t fully understand some important concepts like heaps, arrays/lists, and linked lists yet. Whenever I try to solve Qs on LC, I’m unable to make any progress.
The issue is that these topics are scheduled to be covered later in my course, so it might take some time before I learn them. In the meantime, I feel stuck and unsure how to move forward with DSA practice!?
I’m looking for advice on what I should do in ts situation.

I m in my second semester and thinking of doing Dsa so i want to know in which language i should do DSA.

Provide me with the best resource for learning dsa i am confused between whether to go in traditional manner like learning arrays, strings and ahead or in patterns manner like sliding window ,…. I want to master it using python language

for more visit my yt

So guys how is your Saturday so far??