Deeksha Pandey’s Post

Can’t figure out how to solve DSA problems ? This cheat sheet will give you the right steps to take. 🔹 Input Array is Sorted? => Binary Search => Two Pointers 🔹 Permutations/Subsets? => Backtracking 🔹 Tree Problem? => DFS => BFS 🔹 Graph Problem? => DFS => BFS 🔹 Linked List Problem? => Two Pointers 🔹 No Recursion Allowed? => Use a Stack 🔹 Solve In-Place? => Swap values => Store multiple values in the same pointer 🔹 Max/Min Subarray/Subset/Options? => Dynamic Programming 🔹 Top/Least K Items? => Heap => QuickSelect 🔹 Common Strings? => Map => Trie 🔹 Else? => Use a Map/Set for O(1) time & O(n) space => Sort input for O(nlogn) time & O(1) space Bonus Resources: 📍 Strivers’ Sheet: https://bit.ly/3vydeKg 📍 Leetcode’s Top Interview Questions List: https://bit.ly/3Ktr19i 📍 Interview Experiences from GfG: https://bit.ly/3MKiuQK 📍 Pepcoding's must do interview questions for FAANG: https://bit.ly/3D3E9kD Some lesser-known, but GOLD lists: 📍 Neetcode: https://neetcode.io/ 📍 Sean Prashad’s Leetcode Patterns: https://bit.ly/3KzLsRS 📍 Blind 75: (https://bit.ly/3MDSma8) / Blind 50: (https://bit.ly/3KyN3HU) 📍 work@tech: https://bit.ly/3vWjcno Save this post and ace your next coding interview! 𝗝𝗼𝗶𝗻 𝗺𝘆 𝘁𝗲𝗹𝗲𝗴𝗿𝗮𝗺 𝗰𝗵𝗮𝗻𝗻𝗲𝗹 - https://lnkd.in/d_PjD86B Happy learning !

I solved 300+ DSA problems to learn these 15 problem-solving lessons for arrays and strings. You can learn them in the next 2 minutes. 1. Two Pointers    - Access both ends of an array easily using two pointers. 2. Overwrite Instead of Remove    - Overwrite an element rather than removing it. 3. Watch for Off-By-1 Errors    - Be careful to avoid common off-by-1 mistakes. 4. Reverse Array for Digits    - Place the least significant digit at the beginning by reversing the array. 5. Subarray Tools    - Learn and use utilities to handle subarrays in your language. 6. Preserve Input Array    - Keep the input array unchanged unless necessary. 7. Parallel Logic in Multidimensional Arrays    - Apply parallel logic for each dimension in multidimensional arrays. 8. Efficient Subsets with Arrays    - Use arrays to represent subsets efficiently. 9. Fixed-Length Array for Characters    - Use a 256-length array instead of a hash table for characters. 10. Use StringBuilder     - Efficiently concatenate strings using StringBuilder. 11. Immutable Strings     - Consider using char arrays for string manipulation in languages with immutable strings. 12. Rolling Hash     - Utilize rolling hash for efficient string comparison. 13. Know ASCII Values     - Learn how to get and convert ASCII values and digit characters. 14. Sliding Window Technique     - Improve time complexity from quadratic to linear with sliding windows. 15. Implement Tries     - Use tries for efficient storage and representation of word sets. – P.S: If you need help with:  - DSA & System Design - Interview Preparation  - Mock interviews Book a 1:1 Call With Me. Here’s the Link: https://lnkd.in/eX8cF2aC

Doing 594 Leetcodes won't get you anywhere. 20 patterns to crush your next interviews: 0. Sliding Window: Efficiently compute a target subset in a fixed-size window. 1. Matrix Traversal: Efficiently traverse 2D matrices. 2. Two Pointers: Use two pointers to process input data from two ends. 3. Fast and Slow Pointers: Detect cycles or find the middle in linked structures. 4. Merge Intervals: Work with intervals by merging overlapping ones. 5. Cyclic Sort: Sort numbers when the range is known. 6. In-place Reversal of a LinkedList: Reverse linked lists in-place for space efficiency. 7. Tree BFS: Level order traversal of trees. 8. Tree DFS: Depth-first traversal of trees for various tree-related problems. 9. Two Heaps: Use two heaps to find the median in a data stream. 10. Subsets: Generate all subsets of a given set. 11. Modified Binary Search: Customize binary search to fit problem requirements. 12. Bitwise XOR: Use XOR operations for unique element problems. 13. Top ‘K’ Elements: Use heaps to find the top K elements. 14. K-way Merge: Merge multiple sorted arrays. 15. Topological Sort: Order tasks based on dependency using graph algorithms. 16. Knapsack (Dynamic Programming): Solve optimization problems using dynamic programming. 17. Fibonacci Numbers: Simpler cases of dynamic programming (staircase...) 18. Palindrome Subsequence: Techniques to find or check palindromes with string combinations. 19. Longest Common Substring: Find the longest substring shared by two strings. learn more on Educative - https://lnkd.in/gMwniv-q #programming #coding

The Interview will always look like this:   - Dynamic programming   - Algorithms, data structures, system design   - Design scalable systems and solve complex problems  But the job can look like:  - Fix the typo in the header   - Update the logo on the homepage   - Tweak the button style in CSS  It’s harsh but true. As a candidate, you must keep learning and improving your skills because the industry is getting more competitive, and deep knowledge is now essential. Here are some of the resource posts I’ve done in the past to help you: •Networking Protocols: https://lnkd.in/eRwtjUMC •DSA Patterns: https://lnkd.in/e764gRrF •System Design Resources: https://lnkd.in/eHbSujzY •System Design Fundamentals: https://lnkd.in/e4UaBPMW •System Design Case Studies:  https://lnkd.in/eJDg5hFH •DP, Strings & Tree Leetcode Problems: https://lnkd.in/eGGXwkpM •Array Problem-Solving Cheatsheet:  https://lnkd.in/eS8mmYpB – P.S: Recently, I’ve been getting a lot of queries regarding DSA, HLD, and LLD. I’ve just launched my One Stop Resource guide to help all aspiring software engineers. What will this guide help you with: - full roadmap of DSA, HLD and LLD for interviews - good resources that I used included to save you time - lots of problems and case studies for DSA and system design. I’m running a 35% offer for the first 100 people, here’s the link: https://lnkd.in/e-detVTg

Techniques to Solve a DSA Problem from Brute Force to Optimized 1. Understand the Problem: Clarify inputs, outputs, and edge cases. - Clarify inputs and outputs: Ensure clear understanding of the problem requirements. - Identify edge cases: Consider special scenarios like empty or large inputs. 2. Brute Force Solution: Use a simple, straightforward approach with basic implementation. - Outline a simple approach: Think of the most straightforward way to solve the problem. - Implement the basic solution: Write the brute force code. - Analyze complexity: Determine time and space complexity (often (O(n^2)) or worse). - Test with simple cases: Ensure the solution works on basic test cases. 3. Optimize the Solution: Identify inefficiencies and apply advanced techniques like sorting, hashing, or dynamic programming. - Identify bottlenecks: Find inefficiencies in the brute force solution. - Consider advanced techniques: - Sorting: Can simplify the problem and improve efficiency. - Hashing: Provides faster lookups, reducing time complexity. - Divide and Conquer: Breaks the problem into smaller sub-problems. - Dynamic Programming: Stores intermediate results to avoid redundancy. - Greedy Algorithms: Makes optimal choices at each step. - Advanced Data Structures: Uses heaps, trees, graphs, etc., for specific optimizations. - Outline the optimized approach: Explain how the new method works and why it’s better. - Implement the optimized code: Write the improved algorithm. - Test thoroughly: Run against various test cases, including edge cases and large inputs. 4. Analyze and Review: Verify complexity, discuss trade-offs, and refine the solution. - Complexity analysis: Verify time and space complexity. - Discuss trade-offs: Explain trade-offs made for optimization. - Reflect and review: Check for further optimizations or alternative approaches. By following these steps, you can systematically solve DSA problems efficiently, moving from a basic brute force solution to an optimized implementation.

I solved 300+ DSA problems to learn these 15 problem-solving lessons for arrays and strings. You can learn them in the next 2 minutes. 1. Two Pointers    - Access both ends of an array easily using two pointers. 2. Overwrite Instead of Remove    - Overwrite an element rather than removing it. 3. Watch for Off-By-1 Errors    - Be careful to avoid common off-by-1 mistakes. 4. Reverse Array for Digits    - Place the least significant digit at the beginning by reversing the array. 5. Subarray Tools    - Learn and use utilities to handle subarrays in your language. 6. Preserve Input Array    - Keep the input array unchanged unless necessary. 7. Parallel Logic in Multidimensional Arrays    - Apply parallel logic for each dimension in multidimensional arrays. 8. Efficient Subsets with Arrays    - Use arrays to represent subsets efficiently. 9. Fixed-Length Array for Characters    - Use a 256-length array instead of a hash table for characters. 10. Use StringBuilder     - Efficiently concatenate strings using StringBuilder. 11. Immutable Strings     - Consider using char arrays for string manipulation in languages with immutable strings. 12. Rolling Hash     - Utilize rolling hash for efficient string comparison. 13. Know ASCII Values     - Learn how to get and convert ASCII values and digit characters. 14. Sliding Window Technique     - Improve time complexity from quadratic to linear with sliding windows. 15. Implement Tries     - Use tries for efficient storage and representation of word sets. – P.S: If you need help with:  - DSA & System Design - Interview Preparation  - Mock interviews Book a 1:1 Call With Me. Here’s the Link: https://lnkd.in/eX8cF2aC

In this Blog I try to explain How we can solve different pattern problem in a simple and unique way and by using a simple template. Hope all of you like the blog and enjoy it any further suggestions are most welcome Happy study #Pattern_problem #python #logic_building

Hey connections, I have 14 problem solving DSA patterns for your ease. ☺ . . . . . . 1. Two Pointer Problems: https://lnkd.in/dK_fB-Eg 2. Backtracking Pattern: https://lnkd.in/dDGsdfps 3. Dynamic Programming Patterns: https://lnkd.in/dX7a4cau 4. Dynamic Programming Patterns 2: https://lnkd.in/db2tAp27 5. Powerful Ultimate Binary Search Template: https://lnkd.in/dxk7kdeb 6. A general approach to backtracking questions: https://lnkd.in/drsHxsZh 7. Binary Tree Traversal & Views: https://lnkd.in/dxGcKx65 8. Graph For Beginners [Problems | Pattern | Sample Solutions]: https://lnkd.in/dkpyiB3R 9. A comprehensive guide and template for monotonic stack based problems: https://lnkd.in/dtmFMzDJ 10. All Types of Patterns for Bits Manipulations and How to use it: https://lnkd.in/d-rfVNx2 11. Collections of Important String questions Pattern: https://lnkd.in/dCy_j-vw 12. Leetcode Pattern 1 | BFS + DFS == 25% of the problems: https://lnkd.in/dtaEpzrC 13. Template that can solve most 'substring' problems: https://lnkd.in/dEbVbBu4 14. C++ Maximum Sliding Window Cheat sheet Template: https://lnkd.in/dPiMzzpA Stay curious, keep learning, keep sharing ! #dsa #datastructures #datastructuresandalgorithms #coding #problem #leetcode #patterns #tech

🏆 𝗦𝘁𝗮𝗰𝗸𝘀: 𝗧𝗵𝗲 𝗛𝗲𝗮𝗿𝘁 𝗼𝗳 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗖𝗼𝗱𝗶𝗻𝗴! 🏆 Stacks are one of the simplest yet most powerful linear data structures, making them a must-know for any programmer. Here's what makes stacks indispensable: 🔹 𝗙𝗜𝗟𝗢 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: Stacks follow the "First In, Last Out" rule. Imagine a stack of plates—you can only add or remove plates from the top! Or, think of a woman wearing bangles on her hand; the last bangle added is the first to be removed! 🔹 𝗖𝗼𝗿𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: 1️⃣Push: Add an item to the top of the stack. 2️⃣Pop: Remove the top item. 3️⃣Peek/Top: View the top item without removing it. 🔹 𝗥𝗲𝗮𝗹-𝗟𝗶𝗳𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: 1️⃣Browser Navigation: Moving forward/backward in browsing history. 2️⃣Undo/Redo: Managing edits in text editors. 3️⃣Function Call Stack: Tracking active functions in programming. 🔥 𝗦𝘁𝗮𝗰𝗸𝘀 𝗶𝗻 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴: 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐥𝐨𝐯𝐞𝐬 𝐬𝐭𝐚𝐜𝐤𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞𝐢𝐫 𝐬𝐢𝐦𝐩𝐥𝐢𝐜𝐢𝐭𝐲 𝐚𝐧𝐝 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲. 𝐂𝐨𝐦𝐦𝐨𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞: 🔹Next Greater Element: Quickly find the next greater element for each array element in O(n). 🔹Min Stack: Maintain the minimum value in a stack in O(1). 🔹Stock Span Problem: Calculate how many days you'd have to wait before a higher stock price

A subprocess is a secondary process that is spawned or created by another process, known as the parent process. In the context of computer science and software engineering, subprocesses are often used to achieve multitasking or to delegate specific tasks to separate processes for better organization and efficiency. Here are some key points about subprocesses: 1. **Creation**: Subprocesses are typically created by a parent process using specific system calls or functions provided by the operating system or programming language. For example, in Python, you can create subprocesses using the `subprocess` module. 2. **Independence**: Subprocesses are independent of each other and the parent process. They have their own memory space, execution context, and resources. This means they can run concurrently with the parent process and other subprocesses without interfering with each other. 3. **Communication**: Subprocesses can communicate with the parent process and with each other through various inter-process communication (IPC) mechanisms, such as pipes, sockets, shared memory, or files. This allows them to exchange data and coordinate their activities. 4. **Use Cases**: Subprocesses are commonly used in many scenarios, such as:   - Running external programs or commands from within a parent process.   - Performing computationally intensive or time-consuming tasks in parallel to improve performance.   - Implementing concurrent or parallel processing for tasks like data processing, image processing, or network communication.   - Implementing multi-tiered architectures, where different layers or components of a system are implemented as separate subprocesses. 5. **Termination**: Subprocesses can be terminated either manually by the parent process or by the operating system when they complete their tasks or encounter errors. It's important for the parent process to manage subprocesses properly to avoid resource leaks or unexpected behavior. Overall, subprocesses are a powerful mechanism for achieving concurrency, parallelism, and modularity in software systems, allowing developers to build efficient and scalable applications.