100-Day DSA Challenge: Solving 200 Problems with Binary Search

🔥 Solving 200 Problems in 100-Day DSA Challenge 🚀 Day 11 / 100 — DSA Challenge I have taken up a challenge to solve 200 DSA problems in 100 days. This will not only make me a better engineer but also a better problem solver. Today, I have focused on sorting algorithms. I have covered Bubble Sort and Selection Sort. ✅ Today’s Progress Topic: Sorting Algorithms - Bubble Sort & Selection Sort 💡 Bubble Sort (Optimized) Key Insight: Bubble Sort swaps adjacent elements to bring the largest element to the end of the array. I have optimized the code by adding one more step. We can stop the algorithm if no swaps are made in a pass. This means that the array is already sorted. 💡 Selection Sort Key Insight: Selection Sort selects the minimum element from the unsorted part and places it at the correct position. Time Complexity remains O(n²) in all cases. 🧠 Biggest Insight Today Simple algorithms can also impart valuable concepts such as iteration, swapping, and optimization, which form the basis for more complex algorithms. 💼 Real-World Relevance Sorting is a fundamental operation used in all software systems, including: • Data processing and analysis • Search and filter operations • Backend systems and databases Optimized sorting is critical for smooth operation at scale. 🎯 Why I’m Doing This ✔ Strengthen core CS fundamentals ✔ Enhance my problem-solving skills ✔ Develop the capacity to work on real-world projects ✔ Become an engineer with the capacity to solve real-life problems Consistency beats intensity 🔥 If you’re also looking to enhance your coding skills or preparing for interviews, let’s connect 🤝 #100Daysofcode #Dsa #Algorithms #Datastructures #Problemsolving #Softwareengineering #Programming #Coding #Developers #Computerscience #Fullstackdeveloper #Mern #Webdevelopment

🔥 Solving 200 Problems in 100-Day DSA Challenge 🚀 Day 13 / 100 — DSA Challenge I am undertaking a challenge of solving 200 problems within a period of 100 days. This challenge is meant to make me a better engineer and a better problem solver. Today, I was able to solve an interesting array problem by applying the Dutch National Flag (DNF) algorithm. ✅ Today’s Progress Problem: Sort Colors 💡 Key Insight: The array contains only 0s, 1s, and 2s. It needs to be sorted in place without using extra space and taking O(n) time complexity. Instead of applying a normal sorting algorithm, today’s approach was to use the Dutch National Flag algorithm with three pointers: low → tracks the position of 0 mid → tracks the current element high → tracks the position of 2 Logic: If the element is 0 -> Swap with low Move low and mid If the element is 1 -> Move mid If the element is 2 ->Swap with high Move high 🧠 Biggest Insight Today Sometimes problems that appear to be sorting problems are really problems involving pattern recognition and pointer manipulations rather than traditional sorting algorithms. 💼 Real-World Relevance These types of techniques allow us to process data in-place with minimal memory usage, which can be important if dealing with large data sets. 🎯 Why I’m Doing This ✔ Strengthen my basic computer science concepts ✔ Improve my problem-solving abilities ✔ Gain the ability to work on “real-world” projects ✔ Become an engineer that can solve “real-life” problems Consistency beats intensity 🔥 If you are also looking to improve your coding abilities or get ready for an interview, let’s connect 🤝 #100Daysofcode #Dsa #Algorithms #Datastructures #Problemsolving #Softwareengineering #Programming #Coding #Developers #Computerscience #Fullstackdeveloper #Mern #Webdevelopment

🔥 Solving 200 Problems in 100-Day DSA Challenge 🚀 Day 10 / 100 — DSA Challenge I have started a 100-day challenge to solve 200 problems in this DSA challenge, aiming to become a better engineer and a better problem solver. I am continuing with advanced array problems and learning various optimization techniques. I solved a well-known problem today using binary search. ✅ Today’s Progress Problem: Aggressive Cows Problem 💡 Key Insight: The problem looks like a placement problem, but to solve it in an optimal manner, Binary Search on Answer is required. We want to maximize the minimum distance between cows Search space is from 1 to (maximum position - minimum position) For a distance (mid): Check if it is possible to place all cows with a distance of at least mid If possible, try to get a larger distance (st = mid + 1) Else, try to get a smaller distance (end = mid - 1) Thus, an efficient solution is possible in O(n log range) time. 🧠 Biggest Insight Today I understood that if a problem is to maximize a certain value, Binary Search on Answer is a good approach. 💼 Real World Relevance This kind of problem is relevant in the real world when we need to do the following: • Distribute resources with maximum distance between them • Distribute resources with constraints • Maximize the efficiency of the distribution with constraints 💪 Why I’m Doing This ✔ Improve my core computer science concepts ✔ Improve my problem-solving skills ✔ Develop the ability to apply my skills to real-world problems ✔ Develop into a computer engineer who can solve real-life problems Consistency is key 🔥 If you're also working on improving your programming skills or interview preparation, let's connect 🤝 #100Daysofcode #Dsa #Algorithms #Datastructures #Problemsolving #Softwareengineering #Programming #Coding #Developers #Computerscience #Fullstackdeveloper #Mern #Webdevelopment

🔥 Solving 200 Problems in 100-Day DSA Challenge 🚀 Day 8 / 100 - DSA Challenge I have begun a 100-day challenge where I aim to solve 200 problems in the DSA problem set, 2 problems a day, to make myself a better engineer and a more effective problem solver. I have solved a hard problem today, focusing on optimization and advanced problem-solving techniques. ✅ Today’s Progress Problem - Split Array Largest Sum - Hard 💡 Key Insight: This problem is similar to a partition problem, but the solution is using Binary Search on Answer. The range is between the maximum element and the sum of the array. We validate the answer using a helper function, where: *  If the array is split into k partitions, we reduce the range to the lower number and repeat the process. *  Otherwise, we increase the range. This reduces the time complexity from O(n) to O(n * log(sum)). 🧠 Biggest Insight Today I have learned not to always build the problem, but sometimes define a range on the answer and validate it efficiently. 💼 Real-World Relevance This pattern can be applied in cases where: - Load balancing is necessary - Resource distribution is a problem - The maximum resource usage has to be minimized In all cases, efficiency is a priority along with minimizing maximum resource usage. 🎯 Why I’m Doing This ✔ Improve my foundation in basic CS ✔ Improve my problem-solving skills ✔ Learn to work on real-world projects ✔ Learn to become an engineer who can solve real-life problems Consistency beats intensity 🔥 If you are also improving your coding skills or interview prep, let's connect 🤝 #100Daysofcode #Dsa #Algorithms #Datastructures #Problemsolving #Softwareengineering #Programming #Coding #Developers #Computerscience #Fullstackdeveloper #Mern #Webdevelopment

🚨 Stop Memorizing DSA Problems. Start Seeing Patterns. Most developers struggle with DSA not because it’s hard… …but because they try to solve each problem from scratch. That’s the wrong approach. After solving 100+ problems, I realized something: 👉 You don’t need 100 solutions 👉 You need 10–12 patterns Once you master these, most DSA problems become predictable. 💡 Here are the patterns that cover ~80% of problems: 🔹 Sliding Window Used when dealing with subarrays/substrings 👉 Example: Longest substring without repeating characters 🔹 Two Pointers When working with sorted arrays or pairs 👉 Example: Pair sum, remove duplicates 🔹 Binary Search Not just for search — also for optimization problems 👉 Example: Search in rotated array 🔹 Prefix Sum When frequent range sum queries appear 👉 Example: Subarray sum equals K 🔹 Fast & Slow Pointers (Cycle Detection) 👉 Example: Linked list cycle 🔹 Backtracking When you need all combinations/permutations 👉 Example: Subsets, N-Queens 🔹 Dynamic Programming (DP) When problems have overlapping subproblems 👉 Example: Fibonacci, Knapsack 🔹 Greedy When local optimal choice gives global optimal 👉 Example: Activity selection 🔹 Graph Traversal (BFS/DFS) 👉 Example: Number of islands 🔹 Heap / Priority Queue 👉 Example: Top K elements 🔹 Stack / Monotonic Stack 👉 Example: Next greater element 🔹 Union Find (Disjoint Set) 👉 Example: Detect cycles in graphs 🧠 Real Shift That Changed My Game: Earlier: ❌ “Which problem is this?” Now: ✅ “Which pattern is this?” 🔥 If you're preparing for interviews: Stop solving random questions. Start practicing pattern-wise. That’s how top candidates think. 💬 I’m planning to break down each pattern with real interview questions. Follow me if you want to master DSA without feeling lost. #DSA #CodingInterview #SoftwareEngineering #LeetCode #Programming #Developers #TechCareers #DataStructures #Algorithms #FrontendDeveloper #LearningJourney #DAY81 #DSA-1

🔥 Solving 200 Problems in 100-Day DSA Challenge 🚀 Day 9 / 100 — DSA Challenge I have decided to take up a 100-day challenge to solve 200 problems in DSA to become a better engineer and problem solver. Today, I solved a classic problem related to partitioning with an optimized solution. ✅ Today’s Progress Problem: Painter’s Partition Problem 💡 Key Insight: Solved this problem by using Binary Search on Answer. The problem is to minimize the maximum time taken by any painter. The search space is between the maximum time taken by any painter and the total sum of boards. For each mid value, I checked if I could paint all boards with m painters without taking more than mid time. If I could paint all boards, I set end to mid - 1. If I couldn’t paint all boards, I set end to mid + 1. This problem is reduced to O(n log sum). 🧠 Biggest Insight Today When the problem asks us to minimize the maximum value, it is a strong signal to think in terms of Binary Search on Answer. 💼 Real-World Relevance This pattern is heavily utilized in the real world: • Balanced load of tasks across multiple workers • Balanced distribution of workloads in cloud environments • Scheduling of tasks to optimize maximum execution time The distribution of tasks is of critical importance. 🎯 Why I’m Doing This ✔ Improve my understanding of core CS concepts ✔ Improve my problem-solving skills ✔ Develop the capability to work on real-world projects ✔ Develop into an engineer who can solve real-life problems Consistency is better than intensity 🔥 If you’re also looking to enhance your coding skills or prepare for interviews, let's connect 🤝 #100Daysofcode #Dsa #Algorithms #Datastructures #Problemsolving #Softwareengineering #Programming #Coding #Developers #Computerscience #Fullstackdeveloper #Mern #Webdevelopment

🚀 DSA Journey – Day 4-8 After learning loops and pattern problems, I moved to the next core topic — Arrays. 💡 Why Arrays Matter: Arrays are the foundation of most DSA problems. They are heavily used in: • Searching & Sorting • Sliding Window & Two Pointers • Dynamic Programming • Real-world systems (data storage & processing) 📚 Problems I Solved (Striver A2Z + NeetCode): • Find Maximum & Minimum Element • Check if Array is Sorted • Reverse an Array • Second Largest Element • Move Zeros to End • Remove Duplicates from Sorted Array • Two Sum 🧠 Array Cheatsheet (Beginner Friendly): ✔ Traversal → Always start with a loop (O(n)) ✔ Max/Min → Keep a variable and update while traversing ✔ Reverse → Use two pointers (start & end) ✔ Duplicates → Use two pointers / set ✔ Two Sum → Use HashMap for O(n) optimization ⚡ Common Mistakes to Avoid: • Ignoring edge cases (empty array, single element) • Writing O(n²) when O(n) is possible • Not dry running before coding • Forgetting index-based thinking 🧠 What I Learned: • How to shift from brute force → optimized approach • Importance of time complexity in interviews • Same pattern can solve multiple problems 📌 Takeaway: If you master arrays, you unlock 50% of DSA problem-solving patterns. #DSA #Arrays #ProblemSolving #Java #CodingJourney

3 things I wish I knew before starting DSA Data Structures and Algorithms can feel like a mountain that keeps getting taller as you climb. Looking back, there are three truths I wish I’d embraced on Day 1: 1️⃣ It’s slow. You aren't going to master Dynamic Programming in a weekend. Progress is measured in months, not days. It’s okay if a single LeetCode "Medium" takes you three hours at first. That’s not failure; that’s the process. 2️⃣ It’s frustrating. You will hit walls. You will write code that passes 48/49 test cases and spend an hour finding the one edge case you missed. The "Aha!" moment only comes after the "I have no idea what I’m doing" phase. 3️⃣ It works if you persist. Pattern recognition is a muscle. The more you show up, the more the "magic" starts to look like logic. Consistency beats intensity every single time. If you’re currently in the middle of the grind and feel like you’re not moving fast enough: keep going. The compounding effect of daily practice is real. #SoftwareEngineering #DSA #CodingLife #CareerGrowth #Programming

Growth in DSA is not just about solving problems, but about recognizing patterns faster and applying them smarter. Day 30/100 — Data Structures & Algorithms Journey Continuing from yesterday’s shift, today I focused on strengthening my understanding of patterns by observing how similar problems can be solved using the same approach with slight variations. Instead of jumping directly into coding, I’m training myself to first identify the pattern behind the problem. Today’s Focus: Deep dive into Two Pointer and Sliding Window patterns Understanding when to expand vs shrink a window Identifying repeating structures across different problems Improving decision-making before implementation Why this matters? Because the ability to recognize patterns quickly is what separates a beginner from an efficient problem solver. Key Takeaways: DSA is about recognizing patterns under pressure The first step is identifying the approach, not writing code Small optimizations can make a big difference Thinking before coding improves accuracy This phase is helping me shift from solving problems randomly to solving them strategically. Excited to keep building stronger intuition and mastering patterns 🚀 #Day30 #DSA #LeetCode #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #Consistency

Day 7/100 Today’s problem: Search Insert Position The task was to find the index of a target element in a sorted array. If the element isn’t present, return the index where it should be inserted to maintain the sorted order. Used Binary Search to solve this efficiently in O(log n) time: Compared the target with the middle element Narrowed the search space accordingly If not found, the final pointer position gives the correct insert index A simple problem, but a great reminder that mastering fundamentals is key to solving more complex challenges. Consistency is starting to pay off — one step at a time. If you're also practicing DSA, how do you approach binary search problems? #100DaysOfCode #DSA #BinarySearch #ProblemSolving #CodingJourney #TechLearning #SoftwareEngineering #LearnInPublic #Developers #CodingDaily #GrowthMindset #PlacementPreparation