DSA Roadmap for Beginners

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

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

Starting my journey into Data Structures & Algorithms 🚀 This beginner-friendly guide covers the fundamentals that every aspiring developer should know: • Arrays, Linked Lists & Stacks • Queues & Trees • Sorting & Searching Algorithms • Time & Space Complexity (Big-O) • Problem-solving approaches Building strong DSA basics is the first step toward cracking coding interviews and writing efficient code. Consistency is key — keep learning, keep practicing! All credit goes to the original creater of the material. Feel free to Repost & Follow Himansh S. for more helpful resources. #DataStructures #Algorithms #DSA #CodingJourney #Programming #TechSkills #SoftwareDevelopment #LearnToCode #CareerGrowth

Why Data Structures Matter in Programming 💡 Data Structures are not just a subject—they are the backbone of efficient programming. Here’s why they are important: 🔹 Improve performance – Choosing the right structure (Array, Linked List, Stack, Queue) can reduce time complexity 🔹 Better problem-solving – Helps in writing optimized and scalable code 🔹 Real-world applications – Used in search engines, databases, and operating systems Example: A simple search in an unsorted list takes O(n), but with proper structures like trees or hash tables, it becomes much faster. 👉 Learning DSA is not about memorizing—it’s about understanding how to think logically. #DataStructures #Programming #Coding #ComputerScience #Learning

Day 100/100 – DSA Challenge 🚀 Topic: Two Pointer Key Learning: Over the past 100 days, I stayed consistent, solved problems, and strengthened my understanding of Data Structures & Algorithms Learned from #100DaysOfDSA is Consistency > Motivation , just be consistent In the world of competition, be the competition Small progress every day builds strong problem-solving skills Thanks to everyone who supported during this journey 🙏♥️ What is Two Pointer Technique? It’s an optimized approach where we use two indices (pointers) to traverse data efficiently instead of using nested loops. Types of Two Pointer Patterns 1. Opposite Direction: One pointer at start, one at end Used in: Pair Sum, Palindrome 2 Same Direction (Slow & Fast): Both pointers move forward Used in: Remove duplicates, partitioning #100DaysOfCode #DSA #Coding #ProblemSolving #TwoPointer #Consistency #Learning #Pro GitHub: <https://lnkd.in/dtek96E3> #100DaysOfDSA #ProblemSolving #LinkedInLearning #clanguage #coding #programming #developer #softwareengnieer #datastructure #spu

Mastering the Basics: Linked List in Data Structures Understanding how data is organized is key to becoming a great developer. A Linked List is one of the most fundamental concepts in Data Structures that teaches dynamic memory handling and efficient data flow. Key Takeaways: Each node contains data + pointer to the next node Elements are not stored in contiguous memory Efficient for insertions and deletions compared to arrays Ends with a NULL pointer, marking the end of the list Grasping concepts like Linked Lists builds the foundation for advanced topics and real-world problem solving. #DataStructures #LinkedList #DSA #Programming #Coding #SoftwareEngineering #TechLearning #Developers #ComputerScience #CodingJourney #LearnToCode #TechSkil

Linked Lists finally clicked… in under 30 seconds. Not code. Not definitions.  Just one idea 🔗 Every node = data + pointer  - It doesn’t store position  - It stores the address of the next node That changes everything: - Insert → just update links (O(1))  - Delete → break & reconnect (O(1))  - Access → sequential (O(n), no indexing) 👉 Arrays = contiguous memory  👉 Linked Lists = connected memory And that’s the real difference. Stop memorizing DSA.  Start visualizing how data flows. #DSA #LinkedList #Programming #Coding #ComputerScienc

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

🚀 Linked List Series – Insertion in Middle Continuing my journey in Data Structures, today I worked on Insertion in the Middle of a Linked List. Insertion in the middle means adding a new node at a specific position between existing nodes. This requires proper traversal and careful handling of pointers. 🔹 Key Concepts: ✔ Create a new node ✔ Traverse to the desired position ✔ Adjust pointers of previous node ✔ Link new node with next node This operation helps in understanding pointer manipulation and dynamic memory management in a better way. 💡 Step by step learning, getting better every day! #DataStructures #LinkedList #CodingJourney #BCA #Programming #Learning

Day 66 on LeetCode — Binary Search 🔍✅ Today’s problem focused on one of the most fundamental and powerful techniques in DSA — Binary Search. 🔹 Approach Used in My Solution The goal was to find the index of a target element in a sorted array. Key idea in the solution: • Maintain two pointers: low and high • Calculate mid = low + (high - low) / 2 to avoid overflow • Compare nums[mid] with target:   – If equal → return index   – If greater → search left half   – If smaller → search right half • Continue until the search space is exhausted This approach efficiently reduces the search space by half in every step. ⚡ Complexity: • Time Complexity: O(log n) • Space Complexity: O(1) 💡 Key Takeaways: • Mastered the core concept of divide and conquer • Learned how to safely calculate mid to avoid overflow • Reinforced understanding of searching in sorted arrays efficiently 🔥 Binary Search is a must-know pattern for interviews and advanced problems! #LeetCode #DSA #Algorithms #DataStructures #BinarySearch #Arrays #DivideAndConquer #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney