Big O, Time and Space Complexity: Explained Simply скачать в хорошем качестве

Big O, Time and Space Complexity: Explained Simply

Understanding Big O notation is essential for software engineers, especially those that are interviewing. EQUIPMENT I USE ⌨️ Keyboard: https://amzn.to/3tgO0le 🖱️ Mouse: https://amzn.to/45qLl5T 🖥️ Monitor: https://amzn.to/3PzgWw7 🎧 Headphones: https://amzn.to/3PE5C1S 🎤 Mic: https://amzn.to/3EX9lCx 🪑 Chair: https://amzn.to/3PDDlZ6 BOOKS I RECOMMEND: 📖 Clean Code: https://amzn.to/3rzjnqz 📖 The Singularity is Near: https://amzn.to/3RGjfjO 📖 Superintelligence: https://amzn.to/3M3Zz5R 📖 Deep Work: https://amzn.to/3tdDZFi DISCLAIMER: Links might be affiliate links. As an Amazon Associate I earn from qualifying purchases. There is no additional charge to you, so thank you for supporting my channel! 00:00 - Intro 00:08 - An Analogy 00:27 - Time Complexity 01:11 - Definition 01:21 - Best Case, Worst Case, Expected Case 02:25 - Space Complexity 02:51 - Simplify 03:46 - Academia An engineer who understands Big O notation can more accurately estimate how an algorithm will perform with larger datasets. This predictive ability is crucial in the world of big data, where algorithms must often process vast amounts of information. If an algorithm's performance degrades rapidly with increasing input size, it may not be a suitable choice for large-scale data processing. Conversely, an algorithm that scales well with input size, as indicated by its Big O notation, could be an ideal choice in such circumstances. In addition, Big O notation helps software engineers make informed decisions about trade-offs between different algorithmic solutions. A solution with a faster running time may consume more memory, or vice versa. Understanding Big O notation helps engineers balance these trade-offs, choosing the most suitable algorithm for their specific requirements and constraints. #programming #coding #computerscience