Coding Interview Resources

🌻 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗕𝗶𝗴 𝗢 𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻!

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(n²) - 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.

A programmer's life summed up in one meme 😄😂

Tech interviews ask candidates to invert binary trees while their real job is 90% figuring out why a 3rd-party API returns null sometimes.

🤣😂

Rest API in a nutshell

Algorithms for Coding Interviews 👆

Technologies used by Netflix 👆

💡Use ZIP function to iterate over multiple lists simultaneously 💡

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Devops Cheatsheet 💪

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