Coding Interview Resources
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This channel contains the free resources and solution of coding problems which are usually asked in the interviews.

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Job Interview Cheatsheet ๐Ÿ’ช
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Adding numbers without using '+' in Python
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Devops Cheatsheet ๐Ÿ’ช
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๐Ÿ’กUse ZIP function to iterate over multiple lists simultaneously ๐Ÿ’ก

#pythontips #codingtips #python #pythonprogramming #codesmarter #coding
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Technologies used by Netflix ๐Ÿ‘†
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Algorithms for Coding Interviews ๐Ÿ‘†
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Rest API in a nutshell
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Tech interviews ask candidates to invert binary trees while their real job is 90% figuring out why a 3rd-party API returns null sometimes.
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A programmer's life summed up in one meme ๐Ÿ˜„๐Ÿ˜‚
๐ŸŒป ๐—จ๐—ป๐—ฑ๐—ฒ๐—ฟ๐˜€๐˜๐—ฎ๐—ป๐—ฑ ๐—•๐—ถ๐—ด ๐—ข ๐—ป๐—ผ๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป!

O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position.

O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half.

O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one.

O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort.

O(nยฒ) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list.

O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach.

O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.
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Most Asked Interview Questions with Answers ๐Ÿ’ปโœ…
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