Day 150 → Day 10 of Striver’s A2Z DSA Journey Learnt Time & Space Complexity today: Big-O Notation, Best/Average/Worst Case. Preparing hard for college mid-sem exams too! Consistency is my only cheat code #DSA #StriversA2Z #Java #TimeComplexity

Time Complexity - From Fastest to Slowest O(1) → Constant time Example: Accessing an array element by index O(log n) → Logarithmic time Example: Binary Search O(n) → Linear time Example: Looping through a list once O(n log n) → Linearithmic time Example: Efficient sorting (Merge Sort, Quick Sort) O(n²) → Quadratic time Example: Nested loops comparing each element Rule of thumb: As input size grows, slower complexities become expensive very quickly. Good engineers optimize before it becomes a bottleneck. #Algorithms #TimeComplexity #BigO #Coding #SystemDesign

Day 16 of Strike DSA & Gen AI 🚀 Day 10 of DSA 💻 Learned Time Complexity: • Measure operations, not seconds • Ignore constants • Focus on scalability Hard at first, but clear by the end 🔥 #Day16 #DSA #TimeComplexity #CodingJourney @rohit_negi9 @CoderArmy

We all probably memorize the Big O Notation cheat sheets at some point. But when the input size actually grows, how do we really feel the difference in performance between O(n) and O(log n)? It's actually quite hard to get a concrete visual idea of the whole thing just by reading books or looking at theory. Unless you increase the size of an array yourself and watch the operation count go up, you don't really realize how terrible O(n^2) can get. So, to help you practically feel this, I built an interactive guide for Big O Notation on Vizly. There’s a sandbox inside. You can jump in, select different complexities from O(1) all the way to O(n!) and watch the animations happen. For example, you can see exactly how Binary Search works, how Merge Sort splits an Array and stitches it back together and why Bubble Sort is just so slow - all shown with visualizations. If you're just starting out with Data Structures and Algorithms, or getting ready for interviews, this might help clarify the core concepts. Also, sometimes just seeing the visualization might help the concepts stick better. #Vizly #BigONotation #Algorithms #TimeComplexity

#Day11 #StrikeDSA& #GenAI with Rohit Negi bhaiya Day 11 of my DSA journey ✅ Focused on Time Complexity, covering Best, Worst, Average Case and Big O, Big Ω, Big Θ notations. Building a strong foundation in algorithms and problem-solving. 💻 #DSA #TimeComplexity #BigO #BigOmega

#Day9 #StrikeDSA& #GenAI with Rohit Negi bhaiya. Day 10 ✅ Dove deep into Time Complexity & practiced programs to analyze loops and see how operations grow. Feeling more confident in algorithm analysis! 💡 #100DaysOfCode #TimeComplexity #Programming #CodingJourney #Consistency

⚡ STRIKE – @rohit_negi9 | DSA Lectures 11–12 Lecture 11: Solved more problems on time complexity. Learned Big-O, Big-Ω, Θ with n² examples. Lecture 12: Learned space complexity, basics of sorting, and selection sort. Strengthening DSA fundamentals #STRIKE #DSA #TimeComplexity

Day - 20 of STRIKE Lecture - 12 of DSA #DSA #strike #Consistency #Timecomplexity #LearningJourney

⚡ STRIKE – @rohit_negi9 | DSA (Time Complexity) Solved more problems on time complexity. Learned Big-O (upper bound), Big-Ω (lower bound), and Θ (exact bound). For n²: Big-O → n², n³…, Big-Ω → 1, log n, n, and Θ → n² only. #STRIKE #DSA #TimeComplexity #LearningInPublic

Solved LeetCode Problem - Reverse String - Two Pointer - TimeComplexity O(n) Now moving forward for next problem. Let's Connect. Tellme What's Your Plan of this night #LeetCode #DSA #LearnInPublic #DSASolver #connection

🚀 #Day18 | Strike DSA GenAI By @rohit_negi9 Today was about breaking Time Complexity myths Cleared the real meaning of Big O, Big Ω & Big Θ — they are bounds, not best/avg/worst cases. #DSA #TimeComplexity #LearningInPublic #CodeArmy

Day 25 – DSA Algorithm Analysis• Big-O: Upper Bound (The one we care about most!) • Omega: Lower Bound • Theta: Average/Tight Bound Solved practice questions to see how these apply to actual loops and logic. The foundation is getting solid. @rohit_negi9 #DSA #TimeComplexity

Day 18 | Strike DSA GenAI | Lecture 11 by @rohit_negi9 bhaiya Covered Time Complexity fundamentals: log n, best/average/worst cases, and asymptotic analysis — Big O (upper bound), Theta (exact), Omega (lower bound). #DSA #TimeComplexity #BigO #GenAI #Learning

Day 18 of #StrikeDSA & #GenAI 🚀 Correct understanding 👇 • Big-O → upper bound (equal or above) • Ω → lower bound (equal or below) • Θ → exact bound If Θ = n² O can be n³,n⁴… Ω can be n,1 Myths breaked🔥 #DSA #TimeComplexity

✅Lecture11 Of DSA Course in C with @rohit_negi9 #strike Today's Deep learning on the Time Complexity 🌟Know about the - Big O , Omega and Theta Big 0 is the upper bound and omega is lower bound and Theta is exact . Code smarter, not harder 💻⚡ #BigO #DSA #TimeComplexity

Day 16 | #StrikeDSA 🚀 (Lecture 10) Learned the real meaning of Time Complexity. It’s not about execution time, but how code grows with input. Practiced analysis-based questions — felt easier with DAA background. #DSA #TimeComplexity #LearningInPublic #CodingJourney @rohit_negi9

Lecture 10 of STRIKE DSA • Understood what time complexity really means • Cleared common myths around complexity • Analyzed problems based on input growth, not machine speed Instructor - @rohit_negi9 #DSA #TimeComplexity #CPlusPlus #100DaysOfCode #CodingJourney

Day 16 | Strike DSA GenAI | Lecture 10 by @rohit_negi9 Learned time complexity, how code is executed by the CPU, compiler working, and the difference between machine language and binary code. Practiced analyzing program efficiency. #Strike #DSA #GenAI #TimeComplexity

#TimeComplexity Hello Devs, having difficulty understanding time complexity? I highly recommend you read this blog doabledanny.com/big-o-notati…

#TimeComplexity is not doing all the job, you still have more job to do