Starting with the most fundamental concept: What is an Algorithm? An algorithm is simply a step-by-step process to solve a problem. From sorting data to solving complex problems, algorithms are the backbone of software development. In this short video, I’ve explained: - What is an algorithm - Real-life analogy - Key characteristics - A simple Java example If you're preparing for coding interviews or starting DSA, this is where you begin. For a structured roadmap, practice questions, and complete DSA in Java learning: https://lnkd.in/gMYTHTWN #DSA #Java #Programming #Coding #SoftwareDevelopment #InterviewPreparation
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Have you noticed that the same code can behave differently depending on the input? This is where Best Case, Average Case, and Worst Case Analysis becomes important. In this short video, I explained: - Best case (fastest execution) - Worst case (maximum time) - Average case (typical scenario) - Why worst-case analysis is commonly used Understanding this concept helps you build efficient and reliable algorithms, especially for real-world systems and coding interviews. 👉 Explore structured DSA in Java roadmap + practice: www.quipoin.com #DSA #Java #Programming #Coding #SoftwareEngineering #InterviewPreparation #Algorithms
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🚀 Week 9 Completed – #100DaysOfCode | DSA in Java Continuing the journey with consistent progress and deeper problem-solving. This week I focused on advanced tree concepts and sliding window technique, which are crucial for coding interviews. 📌 Topics Covered This Week: 🔹 Binary Search Trees (BST) Learned BST properties and operations Solved problems like finding the largest BST in a Binary Tree 🔹 Binary Trees (Advanced) Practiced problems like sum of left leaves Strengthened recursive thinking and tree traversal logic 🔹 Sliding Window Technique Applied sliding window approach to optimize subarray problems Implemented maximum subarray sum using sliding window 📌 Previously Covered Foundation: ✔ Arrays, Strings, Bit Manipulation ✔ Recursion & Backtracking ✔ Linked List, Stack, Queue ✔ Greedy Algorithms & Binary Trees 🧠 Key Learnings • Tree-based problems require a strong understanding of recursion and structure • Sliding window significantly reduces time complexity from O(n²) to O(n) • Choosing the right approach is becoming more intuitive with practice • Focus is now shifting towards optimization and pattern recognition 💻 Maintaining a structured GitHub repository with all topics organized step-by-step and consistent commits. Step by step, moving closer to interview-ready DSA skills. #100DaysOfCode #DSA #Java #ProblemSolving #SlidingWindow #BinaryTree #LearningInPublic
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Optimizing problems is what separates good developers from great ones. In this short video, I explained the Prefix Sum Technique, which helps answer range sum queries efficiently. - Precompute cumulative sums in O(n) - Answer queries in O(1) - Widely used in subarray and matrix problems This simple idea can drastically improve performance in real-world scenarios. Explore structured DSA in Java roadmap + practice: www.quipoin.com #DSA #Java #Programming #Coding #Algorithms #SoftwareEngineering #InterviewPreparation
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🚀 Day 7/100 – DSA Journey Today’s focus was on implementing a classic and important problem in number theory — Armstrong Numbers — using Java. 🔍 What I built: A program to find all Armstrong numbers within a given range (100 to 2000). 💡 Approach Breakdown: Iterated through each number in the range Counted the number of digits Extracted each digit and computed its power using Math.pow() Summed the results and compared with the original number Printed the number if it satisfies the Armstrong condition ⚙️ Key Concepts Used: Loops (while, for) Mathematical logic (digit extraction, power calculation) Function design for reusability Clean iteration over a range 📌 Output Observed: 153, 370, 371, 407, 1634 📈 Learning Insight: This problem strengthened my understanding of number manipulation and algorithmic thinking, which are fundamental in DSA and frequently asked in interviews. 🔥 Consistency is the key — building problem-solving skills step by step! #Day7 #100DaysOfCode #DSA #Java #ProblemSolving #CodingJourney #SoftwareDevelopment #LearnInPublic 🙌 Tagging: 10000 Coders Pathulothu Navinder
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Have you ever thought… why some algorithms solve problems in seconds, while others take hours? This is where Analysis of Algorithms becomes important. In this short video, I explained: - Why algorithm efficiency matters - How we compare algorithms - Linear Search vs Binary Search - Importance of scalability Understanding this concept is a game changer for coding interviews and real-world problem solving. Explore structured DSA in Java roadmap + practice: www.quipoin.com #DSA #Java #Programming #Coding #SoftwareEngineering #InterviewPreparation #Developers
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🚀 Cracked the “Between Two Sets” Problem using Java! I recently worked on a problem that really strengthened my understanding of number theory concepts like LCM (Least Common Multiple) and GCD (Greatest Common Divisor). 🔍 Problem Summary: Given two arrays, the task is to find how many integers satisfy: ✔️ All elements of the first array are factors of the integer ✔️ The integer is a factor of all elements of the second array 💡 Approach I Used: 👉 Calculated LCM of the first array 👉 Calculated GCD of the second array 👉 Counted multiples of LCM that perfectly divide the GCD ✨ This optimized approach avoids brute force and improves efficiency — a great example of applying core math concepts in coding! 🧠 Key Learnings: • Practical use of LCM & GCD • Optimization over brute force • Problem-solving mindset for coding interviews 💻 Implemented in Java and tested with multiple cases successfully. 📌 Always exciting to see how mathematical concepts power efficient algorithms! #Java #DataStructures #Algorithms #Coding #ProblemSolving
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🚀 Day 31 of solving DSA by using java : 😎 Today I worked on an interesting problem: finding the maximum number of consecutive 1’s in a binary array. 🔍 Problem Overview: Given an array containing only 0’s and 1’s, the goal is to determine the longest continuous sequence of 1’s. 💡 Approach: The idea is simple and efficient: Traverse the array once Maintain a counter for consecutive 1’s Reset the counter whenever a 0 appears Track the maximum count throughout the iteration ⚡ Why this works: This approach ensures we only pass through the array once, making it: Time Complexity: O(n) Space Complexity: O(1) ✨ Key Learning: Sometimes the most optimal solutions come from simple observations and clean logic rather than complex techniques. #Consistency in solving these small problems really helps in building strong problem-solving skills! #Coding #DSA #ProblemSolving #Algorithms #CodingPractice #100DaysOfCode #LearnToCode #Tech #SoftwareEngineering #Programming #Developers #CodeNewbie #InterviewPrep 🚀
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What if a function could call itself to solve a problem? That’s exactly what Recursion is. In this short video, I explained: - What is recursion - Base case (stops execution) - Recursive case (reduces problem size) - Factorial example - Importance of call stack Recursion is widely used in solving complex problems like trees, graphs, and divide-and-conquer algorithms. Explore structured DSA in Java roadmap + practice: www.quipoin.com #DSA #Java #Programming #Coding #SoftwareEngineering #Recursion #InterviewPreparation
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Today I worked on a problem that involved finding the maximum count of positive and negative numbers in a sorted array. Key idea: Used Binary Search to efficiently find: The first positive number The first non-negative number From this, we can calculate: Count of positive numbers Count of negative numbers Why Binary Search? Because the array is sorted, it helps reduce time complexity to O(log n) instead of scanning the entire array. What I learned: How to apply binary search beyond just finding elements How to think in terms of boundaries (first occurrence logic) Writing clean and optimized Java code 📌 Problem-solving is not just about coding, it's about thinking efficiently. Looking forward to learning more and improving every day! #Java #DSA #BinarySearch #CodingJourney #ProblemSolving #LearningEveryday
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Building problem-solving skills I recently completed a structured set of Java programming exercises designed to strengthen core concepts and improve coding efficiency. 🔍 Key areas covered: • Control structures and logical decision-making • Recursion and iterative problem-solving approaches • Array manipulation and data processing • String operations and pattern recognition • Mathematical and algorithmic problem-solving What I gained from this experience: → Improved logical thinking and debugging skills → Better understanding of time and space efficiency → Ability to approach problems with structured solutions → Stronger foundation for advanced topics like Data Structures This experience reinforced an important lesson: Mastering basics is the fastest way to grow in technology. Looking forward to applying these learnings in real-world development scenarios. G.R NARENDRA REDDY Global Quest Technologies #SoftwareEngineering #Java #ProblemSolving #DataStructuresPrep #Coding
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