Merging Multiple Sorted Arrays in Programming

Daily DSA Update – Day 27 Solved: Climbing Stairs (LeetCode) Today’s problem introduced a classic Dynamic Programming pattern. Problem: You are climbing a staircase with n steps. Each time you can climb either 1 step or 2 steps. The goal is to find how many distinct ways you can reach the top. Approach: At first glance it looks like a simple counting problem, but the key insight is that the number of ways to reach step n depends on the ways to reach step n-1 and step n-2. So the relation becomes: ways(n) = ways(n-1) + ways(n-2) This follows the same pattern as the Fibonacci sequence. By iteratively calculating previous results, we can efficiently compute the answer. Key Learning: Recognizing patterns in problems is very important. Many Dynamic Programming problems are variations of well-known sequences like Fibonacci. What this strengthened: • Understanding Dynamic Programming basics • Identifying recurrence relations • Optimizing solutions using iterative logic • Pattern recognition in problem solving Small problems like these help build strong foundations for more complex DP challenges. On to the next challenge. #DSA #DataStructures #Algorithms #Java #LeetCode #DynamicProgramming #ProblemSolving #CodingJourney

Encapsulation vs 🎭 Abstraction — Simplified with Real-Life Examples In Object-Oriented Programming (OOP), two concepts often confuse beginners: Encapsulation and Abstraction. Let’s break them down in the simplest way possible 👇 🔐 Encapsulation (Data Hiding) Encapsulation is about protecting data by wrapping it with methods and restricting direct access. 👉 Real-life example: ATM Machine You can withdraw cash or check your balance, but you cannot directly access or modify the bank’s internal data. ✔️ Focus: Security & controlled access 🎭 Abstraction (Hiding Complexity) Abstraction is about hiding unnecessary details and showing only what’s important. 👉 Real-life example: Car You use the steering wheel, accelerator, and brakes without knowing how the engine works internally. ✔️ Focus: Simplicity & usability 💡 Key Difference: Encapsulation = “Don’t access this data directly” 🔐 Abstraction = “You don’t need to know how this works” 🎭 🚀 Mastering these concepts is essential for writing clean, secure, and scalable code. #OOP #Programming #Java #Coding #SoftwareDevelopment #ComputerScience #Learning

𝐃𝐚𝐲 𝟑𝟗 – 𝐃𝐒𝐀 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 | 𝐀𝐫𝐫𝐚𝐲𝐬 🚀 Today’s problem focused on finding the minimum path sum in a triangle using Dynamic Programming. 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐒𝐨𝐥𝐯𝐞𝐝 • Triangle 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 • Started from the last row of the triangle • Stored values in a DP array • Moved row by row upward • For each element, added the minimum of its two possible children Formula used: dp[col] = value + min(dp[col], dp[col + 1]) This bottom-up approach avoids recalculating subproblems. 𝐊𝐞𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠𝐬 • Bottom-up DP simplifies many path problems • Only the next row is needed to compute the current row • Space optimization is possible by using a single array • Breaking a problem into smaller overlapping subproblems is the essence of DP 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 • Time: O(n²) • Space: O(n) 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 Dynamic Programming often becomes easier when you start from the bottom and build your way up. 39 days consistent 🚀 On to Day 40. #DSA #Arrays #DynamicProgramming #LeetCode #Java #ProblemSolving #DailyCoding #LearningInPublic #SoftwareDeveloper

Day 18 of My Programming Journey – Time Complexity Today I learned about Time Complexity, which helps measure how efficient an algorithm is as the input size grows. Understanding time complexity helps developers write optimized and scalable code. 📌 What is Time Complexity? Time complexity describes how the running time of an algorithm increases with the size of the input (n). 🔹 Common Time Complexities • O(1) – Constant Time • O(log n) – Logarithmic Time • O(n) – Linear Time • O(n log n) – Linearithmic Time • O(n²) – Quadratic Time 🔹 Types of Time Complexity Analysis ✔ Best Case The minimum time required for an algorithm to run. Example: Finding an element at the first position in linear search. ✔ Average Case The expected time an algorithm takes for typical input. ✔ Worst Case The maximum time an algorithm takes when the input is in the most difficult case. Example: Searching an element at the last position in linear search. 💻 Problems I Practiced • Linear Search • Finding largest element in an array • Nested loop programs (O(n²)) • Comparing different algorithm efficiencies #Programming #Java #TimeComplexity #DSA #CodingJourney #LearningDaily

Day 71 - LeetCode Journey Solved LeetCode 64: Minimum Path Sum (Medium) today — a classic grid-based dynamic programming problem. The challenge is to move from the top-left corner to the bottom-right corner of the grid while minimizing the sum of all numbers along the path. The only allowed moves are right or down, which makes this a perfect candidate for DP. 💡 Core Idea: Each cell represents the minimum cost required to reach that cell. To reach cell (i, j) we can come from: • Top → (i-1, j) • Left → (i, j-1) So the transition becomes: grid[i][j] += min(grid[i-1][j], grid[i][j-1]) By updating the grid in-place, we gradually build the minimum path cost for every cell until we reach the final destination. ⚡ Key Learning Points: • Applying Dynamic Programming on grids • Using in-place optimization to reduce space • Understanding state transition from top and left cells • Achieving O(m × n) time complexity Problems like this build strong foundations for many advanced DP questions involving paths, matrices, and grid traversal. ✅ Stronger understanding of grid DP ✅ Better optimization mindset ✅ Improved problem-solving patterns Step by step, the DSA journey continues 🚀 #LeetCode #DSA #Java #DynamicProgramming #GridDP #Algorithms #ProblemSolving #CodingJourney #Consistency #100DaysOfCode #InterviewPreparation #DeveloperGrowth #KeepCoding

🚀 100 Days DSA Coding Challenge | Day 40 📌 Problem: Implement Queue using Stacks 🧠 What I learned today: • Understanding how to simulate Queue (FIFO) using Stack (LIFO) • Using two stacks to reverse order and maintain queue behavior • Concept of amortized O(1) time complexity • Strengthening fundamentals of data structure design 🛠️ Approach: Use two stacks: in → for pushing elements out → for popping/peeking When out is empty: Transfer all elements from in to out This reverses the order, making it behave like a queue ⏱️ Complexity: Push: O(1) Pop: O(1) (amortized) Peek: O(1) (amortized) Empty: O(1) 💻 Language: Java Today’s takeaway: Understanding how data structures work internally helps us build one structure using another efficiently. 🔄 On to Day 41 🚀 #100DaysOfCode #DSA #Java #Stack #Queue #DataStructures #ProblemSolving #CodingChallenge #LearningInPublic #LeetCode

📚 Learning Journey – Data Structures Today I learned about how arrays work in programming (Java). I explored: • What an array is and how it stores multiple values in a single variable • How array elements are stored in continuous memory locations • How indexing works (starting from 0) • Basic operations like accessing, inserting, and iterating through elements Understanding arrays is an important first step in Data Structures and Algorithms (DSA), and it helps build a strong programming foundation. Excited to keep learning and solving more problems every day! 💻✨ #Java #DSA #Programming #LearningJourney #Coding #ComputerScience

Understanding Data Structures and Object-Oriented Programming is essential for every Computer Science student. To help with that, I created two full playlists covering these topics step by step. 📊 23 lectures ⏱️ 13+ hours of content 💻 Real explanations and examples Playlists: Data Structure: https://lnkd.in/gMmXdBN4 OOP1 (JAVA) : https://lnkd.in/gnpDnjEE If you're learning these topics, feel free to check them out. Feedback and suggestions are always welcome! #ComputerScience #DataStructures #OOP #ACS

Day 17 / 90 — Software Engineering Challenge Today I focused on revising Object-Oriented Programming (OOP) in Python to write more structured and maintainable code. Concepts Revised • Classes and Objects • Encapsulation and Abstraction • Inheritance and Polymorphism • Constructors and method definitions • Instance vs class variables • Writing modular and reusable code • Organizing logic using classes instead of functions • Improving code readability and maintainability Practical Thinking: Understanding how OOP helps in backend development: • Models → represent data (e.g., User, Task) • Services → handle business logic • Better separation of concerns Well-structured code is easier to debug, extend, and maintain. #90DaysOfCode #Python #OOP #SoftwareEngineering #LearningInPublic

🚀 Built a Banking System using Python & OOP Concepts I recently worked on a mini project where I implemented a simple banking system using Python, focusing on core Object-Oriented Programming concepts. 🔹 Key Features: Account creation (Savings & Current) Deposit & Withdrawal functionality Balance checking Display all accounts Unique account number generation 🔹 Concepts Used: Abstraction (using abstract base classes) Encapsulation (private variables for secure data handling) Class & Object design Input validation without using try-except This project helped me understand how real-world systems can be structured using clean OOP principles while maintaining data security and logic separation. 🎯 Next Improvements: Adding PIN-based authentication Storing data using files/database Building an API using FastAPI Would love to hear your feedback and suggestions! #Python #OOP #Programming #SoftwareDevelopment #Projects #Learning #CodingJourney Ajay Miryala 10000 Coders