Big O: How Code Slows as Data Grows

Big O notation is a computer science technique for analyzing how code performs as data gets larger. It's a very handy tool for the working programmer, but it's often shrouded in off-putting mathematics. In this talk, I'll teach you what you need to know about Big-O, and how to use it to keep your programs running well. Big-O helps you choose the data structures and algorithms that will let your code work efficiently even on large data sets. You can understand Big-O even if you aren't a theoretical computer science math nerd. Big-O isn't as mystical as it appears. It's wrapped in mathematical trappings, but doesn't have to be more than a common-sense assessment of how your code will behave. Talk given by Ned Batchelder at PyCon 2018. Thanks to PyCon for giving us permission to post this talk. freeCodeCamp is not associated with this talk. We're just excited to bring more exposure to to it! -- Learn to code for free and get a developer job: https://www.freecodecamp.com Read hundreds of articles on programming: https://medium.freecodecamp.com ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp