Big O notation is used in Computer Science to describe the performance or complexity of an algorithm. Big O specifically describes the worst-case scenario, and can be used to describe the execution time required or the space used (e.g. in memory or on disk) by an algorithm.
Anyone who’s read Programming Pearls or any other Computer Science books and doesn’t have a grounding in Mathematics will have hit a wall when they reached chapters that mention O(N log N) or other seemingly bizarre syntax. Hopefully this article will help you gain an understanding of the basics of Big O and Logarithms.
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About a month after joining ASOS as an engineer and full of fresh-faced naivety, I willingly signed up to join a team taking part in the RetailWeek TechSprint – a 24-hour hackathon aimed at creating innovation for online retail. Armed with my laptop, a toothbrush and a loose grasp of the latest front-end frameworks, I set off for the InterContinental hotel at the O2 for a day and night of programming, pitching and perseverance. These are the things I learnt.
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InnerSource is the practice of taking lessons learnt from the Open-Source community and applying them to the way we develop software within an organisation. In essence, it involves opening up our codebases to all of our teams, accepting contributions and encouraging community-driven development, and it’s something we’ve been trialling in Commerce Digital Tech at ASOS during the last year. Here are our findings.
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