200 Problem 100-Day DSA Challenge: Binary Search Insight

🔥 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

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

DSA Notes: Basics to Advanced Complete Data Structures & Algorithms Master Guide Master Data Structures and Algorithms from basics to advanced with structured and easy-to-understand notes. This complete guide covers arrays, strings, linked lists, stacks, queues, trees, graphs, recursion, sorting, searching, greedy algorithms, and dynamic programming. Perfect for beginners building fundamentals and developers preparing for coding interviews, competitive programming, and top tech companies. Learn step-by-step, revise efficiently, and strengthen your problem-solving skills with this complete DSA learning resource. #DSA #DataStructures #Algorithms #LearnDSA #CodingInterview #ProblemSolving #CompetitiveProgramming #Programming #SoftwareEngineering

🔥 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

𝗦𝘁𝗿𝗶𝘃𝗲𝗿 𝗗𝗦𝗔 𝗡𝗼𝘁𝗲𝘀 – 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 Ace Data Structures and Algorithms with these complete Striver DSA notes! This guide follows the popular A2Z DSA Sheet, covering everything from basic concepts to advanced problem-solving techniques. Learn arrays, strings, recursion, linked lists, trees, graphs, dynamic programming, and more — all in a structured and easy-to-follow format. Perfect for beginners, intermediate learners, and anyone preparing for coding interviews in top tech companies. Practice smart, stay consistent, and level up your coding skills with proven strategies and handpicked problems. Start your DSA journey the right way and build strong problem-solving skills! #StriverDSA #DSA #CodingInterview #LearnCoding #Algorithms #DataStructures #Programming #CodingJourney #TechPrep #InterviewPreparation #CodeDaily #DeveloperLife #ProblemSolving #DSASheet #PlacementPrep

🚀 Two Simple Concepts That Save Time in DSA: Range Count & Sum Formula While solving problems on LeetCode, I realized how often these two basics show up 👇 🔹 1. Count of Numbers in a Range [a, b] Instead of manually counting, just use: 👉 Count = b - a + 1 📌 Example: Range [3, 10] → 10 - 3 + 1 = 8 numbers 🔹 2. Sum of First n Numbers Instead of looping, use: 👉 Sum = n(n + 1) / 2 📌 Example: 1 + 2 + 3 + … + 100 = 5050 💡 Why this matters? Saves time ⏱ Avoids unnecessary loops Helps prevent TLE (Time Limit Exceeded) Frequently used in coding interviews 🔥 Key Learning: Small mathematical optimizations can turn an O(n) solution into O(1) 💬 Next time you see a loop problem, ask yourself: “Can this be solved using a formula instead?” #DSA #CodingInterview #LeetCode #ProblemSolving #Algorithms #Programming #Tech #LearningJourney

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

🔁 Forgetting DSA? Try This Smarter Strategy Instead of Re-learning Everything Most people try to memorize 100s of problems… and forget them anyway ❌ Here’s a better approach that actually works 👇 👉 Focus on Patterns, Not Problems - Stop memorizing solutions - Understand the core patterns behind problems 👉 Identify Core Patterns - Each topic (like Binary Search) has ~3–5 key patterns - Master those instead of solving 20+ random questions 👉 Follow the “2-Problem Rule” - Pick only 2 representative problems per pattern - Understand them deeply (not just code) 👉 Maintain Notes (Notebook / Digital) 🧠 - Use Notebook OR Digital Notes (README / Whiteboard) - Choose what works best for you - Keep it clean, structured & easy to revise - Make sure it’s easily accessible anytime 👉 Weekly Revision (2–3 hours) 🔁 - Revisit your notes every week - This converts knowledge → long-term memory 💡 Why this works? Because your brain remembers patterns + logic, not isolated problems. 🚀 Consistency > Quantity in DSA #DSA #Coding #InterviewPrep #Java #SoftwareEngineering #LeetCode #Developers #Learning #CareerGrowth

Most people don’t struggle with DSA because they’re “bad at coding.” They struggle because they try to memorize hundreds of problems instead of learning the small set of patterns behind them. Once I stopped asking: ❌ “Which LeetCode problem is this?” and started asking: ✅ “Which pattern is hiding here?” everything changed. This cheat sheet covers the core DSA patterns that solve the majority of interview questions: • Two Pointers • Sliding Window • Prefix Sum • Binary Search • Fast & Slow Pointers • Monotonic Stack • Tree Traversal • Heap / Priority Queue • Top K Frequency • Merge Intervals • Hashmaps • DFS / BFS The biggest realization? The same pattern keeps showing up again and again in different forms. A “Longest Substring Without Repeating Characters” problem teaches you Sliding Window. A “Top K Frequent Elements” problem teaches you Heaps. A “Find Peak Element” problem teaches you Binary Search. A “Next Greater Element” problem teaches you Monotonic Stack. You don’t need to master 300 problems. You need to master the patterns. If I had to start over, I’d spend 7 days like this: Day 1: Arrays & Strings Day 2: Binary Search Day 3: Linked Lists Day 4: Stacks & Queues Day 5: Trees Day 6: Heaps / Priority Queues Day 7: Re-solve everything without notes That one week would be more valuable than months of random practice. Quick challenge 👇 Comment with the ONE DSA pattern that changed the way you solve problems. For me, it was Sliding Window — once it clicked, so many problems became easier. What’s yours? Let’s build the best pattern list in the comments 🚀 #DataStructures #Algorithms #DSA #CodingInterview #LeetCode #SoftwareEngineering #Programming #InterviewPrep #ComputerScience #Tech