Big O

Big O is how we measure the worst potential running time of an algorithm: the higher the number and/or how quickly it scales, the worse it is
Scenarios from worst to best:
1. Factorial time (O(n!)) is the worst possbile scenario, but unavoidable in some NP-complete cases.
2. Exponential Growth (2n) is the next worst possible Big O scenario.
3. Quadratic growth (O(n^2)) is next, commonly seen in nested loops.
4. n log n
5. n
6. log n: This is when the complexity is cut in half with each operation, such as binary search.
7. 1
If the algo is "Do this, then do that when you're done with this", you add runtimes.
If the algo is "Do this for every time you do that", you multiply runtimes.

Recursion

Recursion is what we call the technique of a function calling itself until it reaches some predetermined limit.
The base case is what tells the recursive algorithm when to stop calling itself.
The recursive call is the portion of the algorithm where it keeps calling itself until it reaches the base case.
Recursive solutions take less time at the cost of computational space/memory.
Understanding how the call stack works is important to truly understanding recursion. As the recursive call runs, the call stack gets fuller until the base case is reached.
JS DOES NOT have consistent tail recursion support, though it is starting to creep in.