Coding Interview Tips: Problem Solving Strategies

Day 53 :- 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗶𝗻 𝗦𝘁𝗿𝗶𝗻𝗴𝘀: 𝗜𝘀𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗦𝘁𝗿𝗶𝗻𝗴𝘀 🔤🔁 Today’s DSA session was about understanding character mapping and pattern consistency using LeetCode 205: Isomorphic Strings. This problem is a great way to strengthen understanding of hashing + mapping patterns in strings. 🔹 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴 Given two strings s and t, determine if they are isomorphic. 👉 Two strings are isomorphic if characters in s can be replaced to get t, while maintaining order. 👉 No two characters can map to the same character (one-to-one mapping). 🔹 𝗧𝗵𝗲 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 💡 This problem combines: • Character mapping • Tracking last seen positions 👉 Instead of explicitly storing mappings, we track last occurrence index of characters. 🔹 𝗖𝗼𝗿𝗲 𝗟𝗼𝗴𝗶𝗰 ✔️ If lengths are different → return false ✔️ Use two arrays to track last seen positions: • lastS for string s • lastT for string t ✔️ For each index i: • If last occurrence of s[i] ≠ last occurrence of t[i] → not isomorphic • Update both with i + 1 👉 Using i + 1 avoids confusion with default value 0 🔹 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗪𝗼𝗿𝗸𝘀 👉 Ensures consistent mapping pattern 👉 If two characters behaved differently before → mismatch detected 👉 No need for complex hash maps 🔹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 ⚡ • Time Complexity → O(n) • Space Complexity → O(1) (fixed size arrays) 🔹 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 💡 👉 Pattern problems can often be solved using last seen indices 👉 Always think of simpler representations before jumping to hash maps 👉 Consistency in mapping is more important than actual values 🙏 Huge thanks to my mentors Anchal Sharma and Ikshit .. for guiding me in recognizing patterns in string problems. This approach makes many problems much simpler! #DSA #Java #100DaysOfCode #Strings #Hashing #ProblemSolving #Mentorship #LeetCode #SoftwareEngineering #CodingJourney 🚀

Most developers get stuck not because the problem is hard. They get stuck because they don’t have a clear approach. Here’s the exact method I use to solve any coding problem: 1. Understand the problem deeply → Read it twice. Don’t rush. 2. Identify input & output clearly → What are you given? What do you need to return? 3. Start with brute force → Always begin simple. 4. Optimize step-by-step → Use patterns like hashmap, two pointers, stack 5. Write clean code → Readable > clever 6. Dry run with examples → Catch mistakes early 7. Debug like a detective → Trace step by step, don’t panic 8. This approach works for: • LeetCode • Interviews • Real-world debugging This single mindset shift will improve your problem-solving faster than solving 100 random questions. Strong developers don’t just code fast. They think clearly. Follow CodeWithIshwar for daily developer growth - Ishwar Chandra Tiwari | CodeWithIshwar #CodeWithIshwar #leetcode #developers #programming #softwareengineering #coding #webdevelopment #java #javascript #100DaysOfCode

🚀 PART of Coding Practice – Moving All Zeros to the End (Java) Today I solved another practical array problem that is very common in coding interviews 👇 🔍 Problem Statement Given an array, move all the zeros (0s) to the end while maintaining the order of non-zero elements. 💡 My Approach (In-Place Solution) I used a two-step logic: Traverse the array and push all non-zero elements forward Fill the remaining positions with zeros 👉 Time Complexity: O(n) 👉 Space Complexity: O(1) (No extra space used) 🧠 Code Logic in Simple Words Use a variable index to track position for non-zero elements Loop through array: If element ≠ 0 → place it at index, increment index After loop: Fill remaining positions with 0 📌 Sample Input Array: [1, 0, 2, 0, 3] ✅ Output [1, 2, 3, 0, 0] 🧪 Test Cases I Tried ✔️ Test Case 1 Input: [0, 0, 1] Output: [1, 0, 0] ✔️ Test Case 2 (No Zeros) Input: [1, 2, 3] Output: [1, 2, 3] ✔️ Test Case 3 (All Zeros) Input: [0, 0, 0] Output: [0, 0, 0] ✔️ Test Case 4 (Mixed) Input: [4, 0, 5, 0, 0, 6] Output: [4, 5, 6, 0, 0, 0] 🤔 Can We Do It Even Better? Can you solve this using minimum swaps? Can you solve it in one pass only? ❓ Challenge for You 💬 Try solving this: Move all negative numbers to the left and positive to the right (maintain order if possible) 📈 My Learning Today In-place array manipulation Importance of pointer/index tracking Writing efficient O(n) solutions This problem looks simple but teaches real optimization thinking 💯 If you have a better approach, drop it in comments 🙌 #Java #DSA #CodingPractice #Arrays #ProblemSolving #100DaysOfCode #DeveloperJourney #Tech #InterviewPrep #Learning

💡 𝗣𝗿𝗲𝗳𝗶𝘅 𝗦𝘂𝗺𝘀 + 𝗔 𝗦𝘂𝗯𝘁𝗹𝗲 𝗧𝘄𝗶𝘀𝘁 𝗼𝗻 “𝗗𝗶𝘀𝘁𝗶𝗻𝗰𝘁” — 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗗𝗦𝗔 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 Today’s problem looked familiar: count subarrays whose sum is divisible by 𝗸. But one word changed everything - 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁 (by value, not by index). 🧠 𝗧𝗵𝗲 𝗕𝗮𝘀𝗲 𝗜𝗱𝗲𝗮: 𝗣𝗿𝗲𝗳𝗶𝘅 𝗦𝘂𝗺 + 𝗠𝗼𝗱𝘂𝗹𝗼 A classic idea from Introduction to Algorithms: 1. If two prefix sums give the same remainder modulo k, 2. the subarray between them has a sum divisible by k. Using a hashmap of (sum % k) -> frequency, we can count such subarrays in O(n). Simple and elegant - but incomplete. ⚠️ 𝗧𝗵𝗲 𝗖𝗮𝘁𝗰𝗵: 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗩𝗮𝗹𝘂𝗲 𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 For an array like [1, 1, 1], subarrays such as [1] occur at multiple positions. They are different by index, but the same by value. Prefix sums will count all of them. We must count them only once. 💡 𝗧𝗵𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗧𝗵𝗮𝘁 𝗦𝗼𝗹𝘃𝗲𝘀 𝗜𝘁 Since the array is sorted, two subarrays that: 1. end at the same value, and 2. have the same (sum % k) must represent the same sequence. So we track such patterns using a key formed by: (sum % k) and the current value, and subtract these duplicates from the earlier total. ✨ 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 This problem wasn’t about finding valid subarrays - it was about removing overcounted ones cleverly. A great reminder that: Sometimes the trick lies in interpreting “𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁” correctly. #DSA #Algorithms #PrefixSum #Hashing #Java #ProblemSolving #CompetitiveProgramming #CodingInterview #DataStructures #CodingLife #Programmer #Developers #TechInterview #LeetCode #CodeNewbie #100DaysOfCode #LearningInPublic #CodingJourney #InterviewPreparation

🔥 𝗨𝗽𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝗮 𝗛𝗮𝗿𝗱 𝗖𝗼𝗻𝘁𝗲𝘀𝘁 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗧𝗼𝗱𝗮𝘆 — 𝗔 𝗟𝗲𝘀𝘀𝗼𝗻 𝗶𝗻 𝗔𝗿𝗿𝗮𝘆𝘀 & 𝗥𝗼𝘁𝗮𝘁𝗶𝗼𝗻𝘀 Today I upsolved a hard problem that looked like a simple rotation task but turned into a lesson in 𝗱𝗶𝘃𝗶𝘀𝗼𝗿𝘀, 𝗽𝗲𝗿𝗺𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝘀, and 𝘀𝗺𝗮𝗿𝘁 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻. 🧩 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Given an array nums of length n, a number k is sortable if: a. k divides n b. Split the array into blocks of size k c. You can cyclically rotate each block d. After rotations, the whole array becomes non-decreasing Return the sum of all such k. 💡 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 Instead of asking “how to rotate to sort?”, ask: What must be true if sorting by rotations is possible? ⚙️ 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 1. Only check divisors of n (valid block sizes) 2. Create a sorted copy of the array 3. For each block, check if some rotation can match its sorted segment 4. Use the minimum element as an anchor to test rotation alignment 5. If all blocks pass → add k to the answer 📚 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 1. Divisors often appear in partition problems 2. Think from the final state backward 3. Use invariants (like minimum element) as anchors A great reminder that tough problems become simpler when you change perspective. #DSA #Algorithms #ProblemSolving #CompetitiveProgramming #Java #LearningInPublic #DataStructures #Coding #CodingLife #ProgrammerLife #Developers #Tech #InterviewPrep #CodingInterview #LeetCode #CP #Upsolving #DailyCoding #ArrayProblems #MathInCoding #LogicBuilding #AnalyticalThinking

📌Struggling with DSA? You’re probably trying to learn problems… instead of patterns. If you’re preparing for coding interviews or just want to get better at problem solving, this is for you. 🎥 Check out the full video: https://lnkd.in/gyhir5mu #java #softwareengineering #coding #cleancode #interviewpreparation #backenddevelopment #codinginterview #D #codinginterview #problemsolving #datastructures #algorithms #softwareengineering #learntocode #programming

I’ve been getting back into solving algorithm problems recently, and I worked through a pretty challenging one: “Delete Columns to Make Sorted III.” At first glance, it looks like a simple string problem. But once you dig in, it becomes a lot more interesting — it’s really about finding a pattern across multiple sequences. 💡 Key idea I learned: Instead of thinking about deleting columns directly, you can reframe the problem as: ➡️ “What is the longest sequence of columns that keeps all strings in sorted order?” Once you see it that way, it turns into a dynamic programming problem — similar to finding a longest increasing subsequence, but across multiple strings. 🧠 What this reinforced for me: Many “hard” problems become easier when you reframe the question Patterns like DP and subsequences show up everywhere Explaining the solution clearly is just as important as solving it I wrote a full breakdown of my approach here: 👉 https://lnkd.in/giN69zAn I’m currently focused on sharpening my problem-solving and backend thinking again. If you’re working through similar problems or preparing for interviews, I’d be interested to hear how you approach these kinds of challenges. #Java #LeetCode #Algorithms #SoftwareEngineering #ProblemSolving

🚀 Learning DSA: Merge Intervals (LeetCode 56) Today I worked on the classic Merge Intervals problem, which is a very common pattern in coding interviews. Problem: Given a list of intervals [start, end], merge all overlapping intervals and return the non-overlapping intervals. Example: Input: [[1,3], [2,6], [8,10], [15,18]] Output: [[1,6], [8,10], [15,18]] Key Insight: If intervals are sorted by their start time, overlapping intervals will appear next to each other. This allows us to merge them efficiently in a single pass. Approach: 1️⃣ Sort intervals based on the start value using a comparator. 2️⃣ Keep track of the current interval (start, end). 3️⃣ If the next interval overlaps (current.start ≤ previous.end), extend the end. 4️⃣ Otherwise, store the previous interval and start a new one. Time Complexity: ⏱️ O(n log n) due to sorting Space Complexity: O(n) for the result list. This problem helped me better understand the interval merging pattern, which is widely used in many problems like meeting rooms, insert intervals, and scheduling problems. 💡 Key takeaway: Sort intervals first, then merge overlapping ones in a single pass. #DSA #Java #CodingInterview #LeetCode #SoftwareEngineering #ProblemSolving #JavaProgramming #SoftwareEngineering #ProblemSolving #100DaysOfCode #CodingJourney #DeveloperCommunity #TechLearning #ComputerScience

If you understood Part 1, this is your next step. Here are the next set of system design concepts every developer should know. Focused on data, performance, and how systems scale in real-world applications. Same approach. Simple explanations. Real examples. No jargon. If you're preparing for interviews or trying to think like a backend engineer, this will take your understanding one level deeper. Part 2 of the series. More coming next. 📌 Save this for later 🔁 Share with someone learning system design 💬 Which concept should I cover in detail next? #systemdesign #softwareengineering #backenddevelopment #databases #webdevelopment #programming #developers #techlearning #coding #interviewprep

🚀 Still stuck solving Python problems the hard way? This one concept can completely change how you think 👉 It’s called Dynamic Programming (DP) and it separates average coders from top problem solvers. Most developers: ❌ Rely on brute force ❌ Write repetitive recursive code ❌ Struggle with time complexity But top programmers do this instead ✅ Break problems into smaller subproblems ✅ Store results using memoization ✅ Turn exponential solutions ➝ polynomial time 💡 Simple mindset shift: Stop solving the same problem again and again. 🔥 What you’ll actually master: • Recursion vs Dynamic Programming • Memoization & Tabulation • Classic problems (Fibonacci, Knapsack, etc.) • How to recognize DP patterns in interviews 💻 Whether you're preparing for: 📌 Coding Interviews 📌 DSA Practice 📌 Competitive Programming Follow Rohit Kushwaha for valuable content #Python #DynamicProgramming #CodingInterview #DSA #Programming #Tech #LearnToCode #DeveloperGrowth