Q: Google Docs wants to implement an "Undo" feature that reverts the last action performed by the user. Which data structure would you recommend for this? 1. Array 2. Queue 3. Stack 4. Graph

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Let's continue exploring the exciting world of algorithms, data structures, and coding together! 🌐🤖 #CodingCommunity #Algorithms #ComputerScience

#15 - Conclusion Time Complexity is a powerful tool to evaluate and improve your code's efficiency. Choose wisely, code smart, and happy coding! 🚀💻 #TimeComplexityMatters

#14 - Keep Learning Time complexity is a fundamental concept in computer science. Keep learning and improving your algorithmic skills! 📚👩‍💻 #AlwaysLearning

#13 - Choose Wisely Picking the right algorithm can make a huge difference in real-world performance. Measure and choose wisely! ⚖️ #AlgorithmSelection

#12 - Real-world Applications Understanding time complexity is vital in app development, data analysis, AI, and more. It's not just for theory! 🌍💼

#11 - Space Complexity Matters Too Don't forget about space complexity! Efficient code optimizes memory usage. 🧐💾

#10 - How to Determine Time Complexity To decide an algorithm's time complexity, analyze its loops, recursive calls, and understand how they depend on input size. 🔍

#9 - O(n!) - The Factorial Time Some algorithms (like brute-force permutations) have factorial complexity. They're typically slow. Avoid if possible! 🙅‍♂️ #FactorialTime

#8 - O(2^n) - The Exponential Time This is usually a red flag! Algorithms with exponential complexity are often impractical for larger inputs. 🚩 #ExponentialTime

#7 - O(n^2) - The Quadratic Time Nested loops are a common culprit. Be cautious; this can be inefficient for large datasets. 📈📈 #QuadraticTime

#6 - O(n log n) - The Quasilinear Time Many sorting algorithms like Merge Sort have this complexity. It's faster than O(n^2) but slower than O(n). #QuasilinearTime

#5 - O(n) - The Linear Time Iterating through an array takes linear time. Time increases linearly with the input size. 📊 #LinearTime

#4 - O(log n) - The Logarithmic Time Binary search is a classic example. As the data size doubles, it only takes one more step. Efficient! 📉 #LogarithmicTime

#3 - O(1) - The Constant Time Some algorithms execute in constant time, like accessing an array element. Their time doesn't depend on input size. 😎 #ConstantTime

#2 - Big O Notation 🅾️ The Big O notation expresses an algorithm's upper bound on runtime. It's like a "worst-case scenario" for time complexity. 📈

#1 - Why does Time Complexity matter? Time Complexity is crucial in coding. It helps us understand how an algorithm's runtime grows as input size increases. Efficiency matters! ⏱️💡

🧐 Let's dive into the world of algorithms and time complexity! ⏳💻 Get ready for a #CodeTimeComplexity journey in this Twitter thread! 🚀