100-Day DSA Challenge: Dutch National Flag Algorithm

There’s a way most of us learn algorithms… and honestly, it doesn’t stick. We go through lists like this, sorting algorithms, searching algorithms, dynamic programming, graphs, all neatly arranged with definitions, steps, and code. At first, it feels productive. Like you’re covering a lot. But after a while, everything starts to blur. Bubble Sort looks familiar, but you can’t quite explain when to use it. You’ve seen Dijkstra’s Algorithm, but applying it to a real problem feels confusing. Dynamic Programming makes sense while reading, then disappears the next day. The issue isn’t effort. It’s how we approach it. Algorithms aren’t meant to be memorized like definitions. They’re ways of thinking. When you slow down and really look at them: You start to notice patterns. Sorting algorithms aren’t just about arranging numbers, they teach how to compare and iterate efficiently. Divide and conquer shows up in more places than you expect. Dynamic programming is just learning how to stop repeating work. Graph algorithms are basically how we model real-world connections. That shift changes everything. Instead of trying to remember 50 different algorithms, you start understanding a handful of ideas deeply, and suddenly, new problems feel familiar. That’s when things begin to click. If you’re learning DSA right now, it might be worth changing the approach a bit. Less rushing. More connecting. Save this before it disappears. ♻️ Repost for someone who’s currently trying to “cram” algorithms and feeling stuck. Credit: Vishakha Singhal #DataStructuresAndAlgorithms #DSA #Algorithms #Programming #SoftwareEngineerin #ComputerScience #CodingJourney #TechLearning #LearnToCode #Developer #ProblemSolving #TechSkills #SoftwareDevelopment #EngineeringMindset

📚 Day 17/130 — What is Big O Notation? Today in my Daily Tech Learning Series, let’s understand a fundamental concept in algorithms 👇 🔹 What is Big O Notation? Big O Notation is used to describe the performance or efficiency of an algorithm in terms of time or space as input size grows. 🔹 Simple Understanding: 👉 Big O = How an algorithm scales with more data It focuses on the worst-case scenario 🔹 Why is it Important? • Helps compare algorithms 📊 • Identifies efficient solutions ⚡ • Crucial for coding interviews • Used in real-world systems 🔹 Common Big O Examples: • O(1) → Constant (best) • O(log n) → Very efficient • O(n) → Linear • O(n log n) → Good (sorting) • O(n²) → Slow 🔹 Key Idea: 👉 Ignore constants & small terms 👉 Focus on how the algorithm grows Example: 2n + 5 → O(n) 🔹 Real-Life Analogy: 👉 Finding a contact in: Sorted phonebook → faster (log n) Unsorted list → slower (n) 📊 See the diagram below for better understanding. 📌 Tomorrow’s Topic: 👉 Best, Average & Worst Case Complexity #BigO #Algorithms #DataStructures #Programming #Coding #TechLearning #LearningInPublic #Students #Developer

Data Structures & Algorithms — Made Simple Understanding DSA is not about memorizing problems. It is about building a strong foundation to solve real-world challenges efficiently. Start with fundamentals like time and space complexity, and Big O notation. Learn core data structures such as arrays, linked lists, stacks, queues, trees, and graphs. Master algorithms including searching, sorting, recursion, greedy methods, divide and conquer, and dynamic programming. The real growth comes from problem solving: Understand the problem → Plan the approach → Write clean code → Optimize the solution. Consistency is the key. Practice daily and track your improvement. Level up your coding skills step by step. #DSA #Programming #CodingJourney #ProblemSolving #SoftwareDeveloper #Learning #Consistency

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

🚖 𝗘𝘃𝗲𝗿 𝘄𝗼𝗻𝗱𝗲𝗿𝗲𝗱 𝗵𝗼𝘄 𝗨𝗯𝗲𝗿 𝗳𝗶𝗻𝗱𝘀 𝘁𝗵𝗲 𝗳𝗮𝘀𝘁𝗲𝘀𝘁 𝗿𝗼𝘂𝘁𝗲 𝗶𝗻 𝘀𝗲𝗰𝗼𝗻𝗱𝘀? 🚖 What looks like a simple route suggestion on your screen is actually a brilliant use of Graph Algorithms in real-world systems. Every time you book a ride: 📍 Pickup point = Node 📍 Destination = Node 🛣 Roads connecting them = Edges ⏱ Time / distance / traffic = Weights This becomes a 𝗦𝗵𝗼𝗿𝘁𝗲𝘀𝘁 𝗣𝗮𝘁𝗵 𝗣𝗿𝗼𝗯𝗹𝗲𝗺. The system then uses powerful algorithms like: ✨ 𝗗𝗶𝗷𝗸𝘀𝘁𝗿𝗮’𝘀 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 Finds the shortest and fastest route when travel costs are positive. ✨ 𝗔* 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 Makes route finding even faster by using heuristics and estimated distance. ✨ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗪𝗲𝗶𝗴𝗵𝘁 𝗨𝗽𝗱𝗮𝘁𝗲𝘀 Traffic, roadblocks, and tolls constantly change route priorities in real time. The most interesting part? The “shortest route” is not always the smallest distance. Sometimes the best route means: ✅ less traffic ✅ lower fare ✅ faster ETA ✅ fewer signals and turns A classic DSA concept that many of us study for interviews is actually solving real-world problems for millions of rides every day. This is why algorithms are much more than coding questions. They power the products we use daily. #Algorithms #DSA #GraphAlgorithms #Java #SoftwareEngineering #SystemDesign #Tech #Learning #LinkedInTech

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

Nobody told me maths was this important for software engineering. I used to think coding was just about frameworks and syntax. Then I hit a ceiling — interviews, system design, ML concepts — and realised I was missing the foundations. So I mapped out the complete mathematics syllabus every software engineer actually needs: 🔵 ESSENTIAL (start here) → Discrete Mathematics — logic, graph theory, combinatorics → Probability & Statistics — Bayes, distributions, hypothesis testing → Linear Algebra — vectors, matrices, eigenvalues, SVD 🟡 IMPORTANT (for specialisation) → Calculus & Optimisation — gradient descent, backpropagation → Number Theory & Algebra — cryptography, RSA, finite fields → Information Theory — entropy, compression, KL divergence 🟢 USEFUL (separates senior from staff) → Numerical Methods — floating point, ODE solvers, interpolation → Complexity & Formal Languages — automata, P vs NP, amortised analysis 8 modules. 72 topics. Everything the IT industry actually uses. The truth? Most bootcamps skip 90% of this. The engineers who know this stuff get the offers others don't. Save this post. Start with discrete maths. Build from there. What part of maths do you wish you'd learned earlier? 👇 #SoftwareEngineering #Programming #Mathematics #CS #Coding #TechCareer #LearnToCode #Developer

🧠 Data Structures & Algorithms — What Actually Matters (Beyond LeetCode) A lot of developers treat DSA as something you grind for interviews and then forget. But in reality: 👉 DSA is not about solving problems — it’s about thinking clearly under constraints. Here are a few lessons that changed how I approach coding: 🔹 1. Patterns > Problems You don’t need to solve 1000 questions. You need to master patterns: ✔ Sliding Window ✔ Two Pointers ✔ Binary Search ✔ DFS / BFS ✔ Dynamic Programming Once you see patterns, problems start repeating. 🔹 2. Brute Force First, Optimize Later Trying to jump directly to the optimal solution often slows you down. ✔ Start with clarity ✔ Then improve time/space complexity ✔ Think in iterations, not perfection 🔹 3. Complexity is a Mindset It’s not just Big-O — it’s about trade-offs. ✔ Time vs Space ✔ Readability vs Optimization ✔ Precomputation vs On-demand 🔹 4. Debugging > Coding Most people fail not because they can’t solve the problem… …but because they can’t debug their own logic. ✔ Trace small inputs ✔ Write clean code ✔ Avoid over-complication 🔹 5. Consistency Beats Intensity Doing 2–3 problems daily > solving 20 in one day and burning out. 💡 One key takeaway: “DSA is not about memorizing solutions — it’s about training your brain to think in structures.” Curious to know 👇 What’s one DSA pattern that completely changed the way you solve problems? #DSA #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #ProblemSolving #Developers #Tech #Programming #LeetCode

Most DSA problems are not about code. They’re about identifying the right pattern. 📚 How to Identify Which DSA Pattern to Use When I solve a problem, I focus on 3 things 👇 🔹 1. Question (What is being asked?) Subarray / substring → Sliding Window / Prefix Sum Pair in sorted array → Two Pointers Search in sorted space → Binary Search Overlapping intervals → Merge Intervals 👉 The question itself gives strong hints 🔹 2. Output (What do we need?) Maximum / Minimum → Kadane’s / Greedy Count of subarrays → Prefix Sum + HashMap All combinations → Backtracking Shortest path → BFS 👉 Output type often decides the approach 🔹 3. Constraints (Most important) Small input → Brute force may work Large input (10⁵ or more) → Need O(n) or O(log n) Time limit strict → Optimize approach 👉 Constraints tell you what NOT to use 💡 Example: If n = 10⁵ and you’re thinking O(n²)… It’s probably wrong. ⚡ Key Insight: Question tells direction Output tells goal Constraints tell limits Master this, and pattern recognition becomes much easier 🚀 #DSA #algorithms #coding #problemSolving #learning